diff --git a/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/data/jdbc/Hotel.java b/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/data/jdbc/Hotel.java
index 587fa3a8e..6b33c380b 100644
--- a/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/data/jdbc/Hotel.java
+++ b/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/data/jdbc/Hotel.java
@@ -41,7 +41,7 @@ public class Hotel {
     @VectorStoreRecordVector(dimensions = 8, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.EUCLIDEAN_DISTANCE)
     private final List<Float> indexedEuclidean;
 
-    @VectorStoreRecordData
+    @VectorStoreRecordData(isFilterable = true)
     private final List<String> tags;
 
     @VectorStoreRecordData
diff --git a/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/data/redis/RedisVectorStoreTest.java b/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/data/redis/RedisVectorStoreTest.java
index cfff9ea03..93ee1c205 100644
--- a/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/data/redis/RedisVectorStoreTest.java
+++ b/api-test/integration-tests/src/test/java/com/microsoft/semantickernel/tests/data/redis/RedisVectorStoreTest.java
@@ -6,7 +6,6 @@
 import com.microsoft.semantickernel.data.redis.RedisVectorStore;
 import com.microsoft.semantickernel.data.redis.RedisVectorStoreOptions;
 import com.microsoft.semantickernel.data.vectorstorage.VectorStoreRecordCollectionOptions;
-import com.microsoft.semantickernel.tests.data.jdbc.Hotel;
 import com.redis.testcontainers.RedisContainer;
 import org.junit.jupiter.params.ParameterizedTest;
 import org.junit.jupiter.params.provider.EnumSource;
diff --git a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/InMemoryVolatileVectorStore.java b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/InMemoryVolatileVectorStore.java
index 513ab72f0..ef4fe9549 100644
--- a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/InMemoryVolatileVectorStore.java
+++ b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/InMemoryVolatileVectorStore.java
@@ -6,11 +6,10 @@
 import com.azure.core.credential.AzureKeyCredential;
 import com.azure.core.credential.KeyCredential;
 import com.microsoft.semantickernel.aiservices.openai.textembedding.OpenAITextEmbeddingGenerationService;
-import com.microsoft.semantickernel.data.textsearch.TextSearchResultValue;
+import com.microsoft.semantickernel.data.vectorsearch.VectorSearchResults;
 import com.microsoft.semantickernel.data.vectorstorage.VectorStoreRecordCollection;
 import com.microsoft.semantickernel.data.VolatileVectorStore;
 import com.microsoft.semantickernel.data.VolatileVectorStoreRecordCollectionOptions;
-import com.microsoft.semantickernel.data.VectorStoreTextSearch;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordData;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordKey;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordVector;
@@ -43,7 +42,6 @@ static class GitHubFile {
         @VectorStoreRecordData
         private final String description;
         @VectorStoreRecordData
-        @TextSearchResultValue
         private final String link;
         @VectorStoreRecordVector(dimensions = EMBEDDING_DIMENSIONS, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.COSINE_DISTANCE)
         private final List<Float> embedding;
@@ -125,24 +123,27 @@ public static void inMemoryStoreAndSearch(
             .then(storeData(collection, embeddingGeneration, sampleData()))
             .block();
 
-        // Build a vectorized search
-        var vectorStoreTextSearch = VectorStoreTextSearch.<GitHubFile>builder()
-            .withVectorizedSearch(collection)
-            .withTextEmbeddingGenerationService(embeddingGeneration)
-            .build();
-
         // Search for results
         // Volatile store executes an exhaustive search, for approximate search use Azure AI Search, Redis or JDBC with PostgreSQL
-        String query = "How to get started?";
-        var results = vectorStoreTextSearch.searchAsync(query, null)
-            .block();
+        var results = search("How to get started", collection, embeddingGeneration).block();
 
         if (results == null || results.getTotalCount() == 0) {
             System.out.println("No search results found.");
             return;
         }
+        var searchResult = results.getResults().get(0);
+        System.out.printf("Search result with score: %f.%n Link: %s, Description: %s%n",
+            searchResult.getScore(), searchResult.getRecord().link,
+            searchResult.getRecord().description);
+    }
 
-        System.out.printf("Best result for '%s': %s%n", query, results.getResults().get(0));
+    private static Mono<VectorSearchResults<GitHubFile>> search(
+        String searchText,
+        VectorStoreRecordCollection<String, GitHubFile> recordCollection,
+        OpenAITextEmbeddingGenerationService embeddingGeneration) {
+        // Generate embeddings for the search text and search for the closest records
+        return embeddingGeneration.generateEmbeddingAsync(searchText)
+            .flatMap(r -> recordCollection.searchAsync(r.getVector(), null));
     }
 
     private static Mono<List<String>> storeData(
diff --git a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithAzureAISearch.java b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithAzureAISearch.java
index 9b2e70acd..382bf0f83 100644
--- a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithAzureAISearch.java
+++ b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithAzureAISearch.java
@@ -5,16 +5,17 @@
 import com.azure.ai.openai.OpenAIClientBuilder;
 import com.azure.core.credential.AzureKeyCredential;
 import com.azure.core.credential.KeyCredential;
+import com.azure.core.util.ClientOptions;
+import com.azure.core.util.MetricsOptions;
+import com.azure.core.util.TracingOptions;
 import com.azure.search.documents.indexes.SearchIndexAsyncClient;
 import com.azure.search.documents.indexes.SearchIndexClientBuilder;
-import com.fasterxml.jackson.annotation.JsonProperty;
 import com.microsoft.semantickernel.aiservices.openai.textembedding.OpenAITextEmbeddingGenerationService;
 import com.microsoft.semantickernel.data.azureaisearch.AzureAISearchVectorStore;
 import com.microsoft.semantickernel.data.azureaisearch.AzureAISearchVectorStoreOptions;
 import com.microsoft.semantickernel.data.azureaisearch.AzureAISearchVectorStoreRecordCollectionOptions;
-import com.microsoft.semantickernel.data.textsearch.TextSearchResultValue;
+import com.microsoft.semantickernel.data.vectorsearch.VectorSearchResults;
 import com.microsoft.semantickernel.data.vectorstorage.VectorStoreRecordCollection;
-import com.microsoft.semantickernel.data.VectorStoreTextSearch;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordData;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordKey;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordVector;
@@ -49,12 +50,11 @@ public class VectorStoreWithAzureAISearch {
     private static final int EMBEDDING_DIMENSIONS = 1536;
 
     static class GitHubFile {
-        @VectorStoreRecordKey()
+        @VectorStoreRecordKey
         private final String id;
-        @VectorStoreRecordData()
+        @VectorStoreRecordData
         private final String description;
         @VectorStoreRecordData
-        @TextSearchResultValue
         private final String link;
         @VectorStoreRecordVector(dimensions = EMBEDDING_DIMENSIONS, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.COSINE_SIMILARITY)
         private final List<Float> embedding;
@@ -64,10 +64,10 @@ public GitHubFile() {
         }
 
         public GitHubFile(
-            @JsonProperty("fileId") String id,
-            @JsonProperty("description") String description,
-            @JsonProperty("link") String link,
-            @JsonProperty("embedding") List<Float> embedding) {
+            String id,
+            String description,
+            String link,
+            List<Float> embedding) {
             this.id = id;
             this.description = description;
             this.link = link;
@@ -108,6 +108,7 @@ public static void main(String[] args) {
         var searchClient = new SearchIndexClientBuilder()
             .endpoint(AZURE_AI_SEARCH_ENDPOINT)
             .credential(new AzureKeyCredential(AZURE_AISEARCH_KEY))
+            .clientOptions(clientOptions())
             .buildAsyncClient();
 
         storeAndSearch(searchClient, embeddingGeneration);
@@ -137,24 +138,27 @@ public static void storeAndSearch(
             .then(storeData(collection, embeddingGeneration, sampleData()))
             .block();
 
-        // Build a vectorized search
-        var vectorStoreTextSearch = VectorStoreTextSearch.<GitHubFile>builder()
-            .withVectorizedSearch(collection)
-            .withTextEmbeddingGenerationService(embeddingGeneration)
-            .build();
-
         // Search for results
         // Might need to wait for the data to be indexed
-        String query = "How to get started?";
-        var results = vectorStoreTextSearch.searchAsync(query, null)
-            .block();
+        var results = search("How to get started", collection, embeddingGeneration).block();
 
         if (results == null || results.getTotalCount() == 0) {
             System.out.println("No search results found.");
             return;
         }
+        var searchResult = results.getResults().get(0);
+        System.out.printf("Search result with score: %f.%n Link: %s, Description: %s%n",
+            searchResult.getScore(), searchResult.getRecord().link,
+            searchResult.getRecord().description);
+    }
 
-        System.out.printf("Best result for '%s': %s%n", query, results.getResults().get(0));
+    private static Mono<VectorSearchResults<GitHubFile>> search(
+        String searchText,
+        VectorStoreRecordCollection<String, GitHubFile> recordCollection,
+        OpenAITextEmbeddingGenerationService embeddingGeneration) {
+        // Generate embeddings for the search text and search for the closest records
+        return embeddingGeneration.generateEmbeddingAsync(searchText)
+            .flatMap(r -> recordCollection.searchAsync(r.getVector(), null));
     }
 
     private static Mono<List<String>> storeData(
@@ -197,4 +201,11 @@ private static Map<String, String> sampleData() {
                         "README: README associated with a sample chat summary react-based webapp" },
         }).collect(Collectors.toMap(element -> element[0], element -> element[1]));
     }
+
+    private static ClientOptions clientOptions() {
+        return new ClientOptions()
+            .setTracingOptions(new TracingOptions())
+            .setMetricsOptions(new MetricsOptions())
+            .setApplicationId("Semantic-Kernel");
+    }
 }
diff --git a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithJDBC.java b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithJDBC.java
index 3a393ad29..b9ff7aa91 100644
--- a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithJDBC.java
+++ b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithJDBC.java
@@ -9,15 +9,14 @@
 import com.microsoft.semantickernel.data.jdbc.JDBCVectorStore;
 import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreOptions;
 import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreRecordCollectionOptions;
-import com.microsoft.semantickernel.data.jdbc.mysql.MySQLVectorStoreQueryProvider;
-import com.microsoft.semantickernel.data.textsearch.TextSearchResultValue;
+import com.microsoft.semantickernel.data.jdbc.postgres.PostgreSQLVectorStoreQueryProvider;
+import com.microsoft.semantickernel.data.vectorsearch.VectorSearchResults;
 import com.microsoft.semantickernel.data.vectorstorage.VectorStoreRecordCollection;
-import com.microsoft.semantickernel.data.VectorStoreTextSearch;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordData;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordKey;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordVector;
 import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
-import com.mysql.cj.jdbc.MysqlDataSource;
+
 import java.nio.charset.StandardCharsets;
 import java.sql.SQLException;
 import java.util.Arrays;
@@ -27,6 +26,7 @@
 import java.util.Map;
 import java.util.stream.Collectors;
 
+import org.postgresql.ds.PGSimpleDataSource;
 import reactor.core.publisher.Flux;
 import reactor.core.publisher.Mono;
 
@@ -42,12 +42,11 @@ public class VectorStoreWithJDBC {
     private static final int EMBEDDING_DIMENSIONS = 1536;
 
     static class GitHubFile {
-        @VectorStoreRecordKey()
+        @VectorStoreRecordKey
         private final String id;
-        @VectorStoreRecordData()
+        @VectorStoreRecordData
         private final String description;
         @VectorStoreRecordData
-        @TextSearchResultValue
         private final String link;
         @VectorStoreRecordVector(dimensions = EMBEDDING_DIMENSIONS, distanceFunction = DistanceFunction.COSINE_DISTANCE)
         private final List<Float> embedding;
@@ -89,8 +88,8 @@ static String encodeId(String realId) {
         }
     }
 
-    // Run a MySQL server with:
-    // docker run -d --name mysql-container -e MYSQL_ROOT_PASSWORD=root -e MYSQL_DATABASE=sk -p 3306:3306 mysql:latest
+    // Run a PostgreSQL server with:
+    // docker run -d --name pgvector-container -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=root -e POSTGRES_DB=sk -p 5432:5432 pgvector/pgvector:pg17
 
     public static void main(String[] args) throws SQLException {
         System.out.println("==============================================================");
@@ -123,14 +122,14 @@ public static void main(String[] args) throws SQLException {
 
     public static void storeAndSearch(OpenAITextEmbeddingGenerationService embeddingGeneration) {
         // Configure the data source
-        var dataSource = new MysqlDataSource();
-        dataSource.setUrl("jdbc:mysql://localhost:3306/sk");
+        PGSimpleDataSource dataSource = new PGSimpleDataSource();
+        dataSource.setUrl("jdbc:postgresql://localhost:5432/sk");
+        dataSource.setUser("postgres");
         dataSource.setPassword("root");
-        dataSource.setUser("root");
 
         // Build a query provider
         // Other available query providers are PostgreSQLVectorStoreQueryProvider and SQLiteVectorStoreQueryProvider
-        var queryProvider = MySQLVectorStoreQueryProvider.builder()
+        var queryProvider = PostgreSQLVectorStoreQueryProvider.builder()
             .withDataSource(dataSource)
             .build();
 
@@ -155,23 +154,26 @@ public static void storeAndSearch(OpenAITextEmbeddingGenerationService embedding
             .then(storeData(collection, embeddingGeneration, sampleData()))
             .block();
 
-        // Build a vectorized search
-        var vectorStoreTextSearch = VectorStoreTextSearch.<GitHubFile>builder()
-            .withVectorizedSearch(collection)
-            .withTextEmbeddingGenerationService(embeddingGeneration)
-            .build();
-
         // Search for results
-        String query = "How to get started?";
-        var results = vectorStoreTextSearch.searchAsync(query, null)
-            .block();
+        var results = search("How to get started", collection, embeddingGeneration).block();
 
         if (results == null || results.getTotalCount() == 0) {
             System.out.println("No search results found.");
             return;
         }
+        var searchResult = results.getResults().get(0);
+        System.out.printf("Search result with score: %f.%n Link: %s, Description: %s%n",
+            searchResult.getScore(), searchResult.getRecord().link,
+            searchResult.getRecord().description);
+    }
 
-        System.out.printf("Best result for '%s': %s%n", query, results.getResults().get(0));
+    private static Mono<VectorSearchResults<GitHubFile>> search(
+        String searchText,
+        VectorStoreRecordCollection<String, GitHubFile> recordCollection,
+        OpenAITextEmbeddingGenerationService embeddingGeneration) {
+        // Generate embeddings for the search text and search for the closest records
+        return embeddingGeneration.generateEmbeddingAsync(searchText)
+            .flatMap(r -> recordCollection.searchAsync(r.getVector(), null));
     }
 
     private static Mono<List<String>> storeData(
diff --git a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithRedis.java b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithRedis.java
index b037f6f74..b6bebff81 100644
--- a/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithRedis.java
+++ b/samples/semantickernel-concepts/semantickernel-syntax-examples/src/main/java/com/microsoft/semantickernel/samples/syntaxexamples/memory/VectorStoreWithRedis.java
@@ -10,9 +10,8 @@
 import com.microsoft.semantickernel.data.redis.RedisStorageType;
 import com.microsoft.semantickernel.data.redis.RedisVectorStore;
 import com.microsoft.semantickernel.data.redis.RedisVectorStoreOptions;
-import com.microsoft.semantickernel.data.textsearch.TextSearchResultValue;
+import com.microsoft.semantickernel.data.vectorsearch.VectorSearchResults;
 import com.microsoft.semantickernel.data.vectorstorage.VectorStoreRecordCollection;
-import com.microsoft.semantickernel.data.VectorStoreTextSearch;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordData;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordKey;
 import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordVector;
@@ -42,12 +41,11 @@ public class VectorStoreWithRedis {
     private static final int EMBEDDING_DIMENSIONS = 1536;
 
     public static class GitHubFile {
-        @VectorStoreRecordKey()
+        @VectorStoreRecordKey
         private final String id;
-        @VectorStoreRecordData()
+        @VectorStoreRecordData
         private final String description;
         @VectorStoreRecordData
-        @TextSearchResultValue
         private final String link;
         @VectorStoreRecordVector(dimensions = EMBEDDING_DIMENSIONS, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.COSINE_DISTANCE)
         private final List<Float> embedding;
@@ -146,23 +144,27 @@ public static void storeAndSearch(
             .then(storeData(collection, embeddingGeneration, sampleData()))
             .block();
 
-        // Build a vectorized search
-        var vectorStoreTextSearch = VectorStoreTextSearch.<GitHubFile>builder()
-            .withVectorizedSearch(collection)
-            .withTextEmbeddingGenerationService(embeddingGeneration)
-            .build();
-
         // Search for results
-        String query = "How to get started?";
-        var results = vectorStoreTextSearch.searchAsync(query, null)
-            .block();
+        // Might need to wait for the data to be indexed
+        var results = search("How to get started", collection, embeddingGeneration).block();
 
         if (results == null || results.getTotalCount() == 0) {
             System.out.println("No search results found.");
             return;
         }
+        var searchResult = results.getResults().get(0);
+        System.out.printf("Search result with score: %f.%n Link: %s, Description: %s%n",
+            searchResult.getScore(), searchResult.getRecord().link,
+            searchResult.getRecord().description);
+    }
 
-        System.out.printf("Best result for '%s': %s%n", query, results.getResults().get(0));
+    private static Mono<VectorSearchResults<GitHubFile>> search(
+        String searchText,
+        VectorStoreRecordCollection<String, GitHubFile> recordCollection,
+        OpenAITextEmbeddingGenerationService embeddingGeneration) {
+        // Generate embeddings for the search text and search for the closest records
+        return embeddingGeneration.generateEmbeddingAsync(searchText)
+            .flatMap(r -> recordCollection.searchAsync(r.getVector(), null));
     }
 
     private static Mono<List<String>> storeData(
diff --git a/samples/semantickernel-learn-resources/pom.xml b/samples/semantickernel-learn-resources/pom.xml
index 6978bdeee..d788980e4 100644
--- a/samples/semantickernel-learn-resources/pom.xml
+++ b/samples/semantickernel-learn-resources/pom.xml
@@ -29,6 +29,19 @@
             <artifactId>semantickernel-api</artifactId>
         </dependency>
 
+        <dependency>
+            <groupId>com.microsoft.semantic-kernel</groupId>
+            <artifactId>semantickernel-data-azureaisearch</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>com.microsoft.semantic-kernel</groupId>
+            <artifactId>semantickernel-data-jdbc</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>com.microsoft.semantic-kernel</groupId>
+            <artifactId>semantickernel-data-redis</artifactId>
+        </dependency>
+
         <dependency>
             <groupId>org.apache.logging.log4j</groupId>
             <artifactId>log4j-api</artifactId>
diff --git a/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/index/Hotel.java b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/index/Hotel.java
new file mode 100644
index 000000000..6fff7664f
--- /dev/null
+++ b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/index/Hotel.java
@@ -0,0 +1,60 @@
+// Copyright (c) Microsoft. All rights reserved.
+package com.microsoft.semantickernel.samples.documentationexamples.data.index;
+
+import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordData;
+import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordKey;
+import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordVector;
+import com.microsoft.semantickernel.data.vectorstorage.definition.DistanceFunction;
+import com.microsoft.semantickernel.data.vectorstorage.definition.IndexKind;
+
+import java.util.Collections;
+import java.util.List;
+
+public class Hotel {
+    @VectorStoreRecordKey
+    private String hotelId;
+
+    @VectorStoreRecordData(isFilterable = true)
+    private String name;
+
+    @VectorStoreRecordData(isFullTextSearchable = true)
+    private String description;
+
+    @VectorStoreRecordVector(dimensions = 4, indexKind = IndexKind.HNSW, distanceFunction = DistanceFunction.COSINE_DISTANCE)
+    private List<Float> descriptionEmbedding;
+
+    @VectorStoreRecordData(isFilterable = true)
+    private List<String> tags;
+
+    public Hotel() {
+    }
+
+    public Hotel(String hotelId, String name, String description, List<Float> descriptionEmbedding,
+        List<String> tags) {
+        this.hotelId = hotelId;
+        this.name = name;
+        this.description = description;
+        this.descriptionEmbedding = Collections.unmodifiableList(descriptionEmbedding);
+        this.tags = Collections.unmodifiableList(tags);
+    }
+
+    public String getHotelId() {
+        return hotelId;
+    }
+
+    public String getName() {
+        return name;
+    }
+
+    public String getDescription() {
+        return description;
+    }
+
+    public List<Float> getDescriptionEmbedding() {
+        return descriptionEmbedding;
+    }
+
+    public List<String> getTags() {
+        return tags;
+    }
+}
diff --git a/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/index/Main.java b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/index/Main.java
new file mode 100644
index 000000000..414c9be8c
--- /dev/null
+++ b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/index/Main.java
@@ -0,0 +1,73 @@
+// Copyright (c) Microsoft. All rights reserved.
+package com.microsoft.semantickernel.samples.documentationexamples.data.index;
+
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStore;
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreOptions;
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreRecordCollectionOptions;
+import com.microsoft.semantickernel.data.jdbc.postgres.PostgreSQLVectorStoreQueryProvider;
+import com.microsoft.semantickernel.data.vectorstorage.options.VectorSearchOptions;
+import org.postgresql.ds.PGSimpleDataSource;
+import reactor.core.publisher.Mono;
+
+import java.util.List;
+
+public class Main {
+    public static void main(String[] args) {
+        // Create a PostgreSQL data source
+        PGSimpleDataSource dataSource = new PGSimpleDataSource();
+        dataSource.setUrl("jdbc:postgresql://localhost:5432/sk");
+        dataSource.setUser("postgres");
+        dataSource.setPassword("root");
+
+        // Create a JDBC vector store
+        var vectorStore = JDBCVectorStore.builder()
+            .withDataSource(dataSource)
+            .withOptions(
+                JDBCVectorStoreOptions.builder()
+                    .withQueryProvider(PostgreSQLVectorStoreQueryProvider.builder()
+                        .withDataSource(dataSource)
+                        .build())
+                    .build())
+            .build();
+
+        // Get a collection from the vector store
+        var collection = vectorStore.getCollection("skhotels",
+            JDBCVectorStoreRecordCollectionOptions.<Hotel>builder()
+                .withRecordClass(Hotel.class)
+                .build());
+
+        // Create the collection if it doesn't exist yet.
+        collection.createCollectionAsync().block();
+
+        // Upsert a record.
+        var description = "A place where everyone can be happy";
+        var hotelId = "1";
+        var hotel = new Hotel(
+            hotelId,
+            "Hotel Happy",
+            description,
+            generateEmbeddingsAsync(description).block(),
+            List.of("luxury", "pool"));
+
+        collection.upsertAsync(hotel, null).block();
+
+        // Retrieve the upserted record.
+        var retrievedHotel = collection.getAsync(hotelId, null).block();
+
+        // Generate a vector for your search text, using your chosen embedding generation implementation.
+        // Just showing a placeholder method here for brevity.
+        var searchVector = generateEmbeddingsAsync(
+            "I'm looking for a hotel where customer happiness is the priority.").block();
+
+        // Do the search.
+        var searchResult = collection.searchAsync(searchVector, VectorSearchOptions.builder()
+            .withTop(1).build()).block();
+
+        Hotel record = searchResult.getResults().get(0).getRecord();
+        System.out.printf("Found hotel description: %s\n", record.getDescription());
+    }
+
+    private static Mono<List<Float>> generateEmbeddingsAsync(String text) {
+        return Mono.just(List.of(1.0f, 2.0f, 3.0f, 4.0f));
+    }
+}
diff --git a/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/recorddefinition/Main.java b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/recorddefinition/Main.java
new file mode 100644
index 000000000..cf6ad56ba
--- /dev/null
+++ b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/recorddefinition/Main.java
@@ -0,0 +1,58 @@
+// Copyright (c) Microsoft. All rights reserved.
+package com.microsoft.semantickernel.samples.documentationexamples.data.recorddefinition;
+
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStore;
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreOptions;
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreRecordCollectionOptions;
+import com.microsoft.semantickernel.data.jdbc.postgres.PostgreSQLVectorStoreQueryProvider;
+import com.microsoft.semantickernel.data.vectorstorage.definition.*;
+import com.microsoft.semantickernel.samples.documentationexamples.data.index.Hotel;
+import org.postgresql.ds.PGSimpleDataSource;
+
+import java.util.Arrays;
+import java.util.List;
+
+public class Main {
+    public static void main(String[] args) {
+        // Create a PostgreSQL data source
+        PGSimpleDataSource dataSource = new PGSimpleDataSource();
+        dataSource.setUrl("jdbc:postgresql://localhost:5432/sk");
+        dataSource.setUser("postgres");
+        dataSource.setPassword("root");
+
+        // Create a JDBC vector store
+        var vectorStore = JDBCVectorStore.builder()
+            .withDataSource(dataSource)
+            .withOptions(
+                JDBCVectorStoreOptions.builder()
+                    .withQueryProvider(PostgreSQLVectorStoreQueryProvider.builder()
+                        .withDataSource(dataSource)
+                        .build())
+                    .build())
+            .build();
+
+        var hotelDefinition = VectorStoreRecordDefinition.fromFields(
+            Arrays.asList(
+                VectorStoreRecordKeyField.builder().withName("hotelId").withFieldType(String.class)
+                    .build(),
+                VectorStoreRecordDataField.builder()
+                    .withName("name")
+                    .withFieldType(String.class)
+                    .isFilterable(true).build(),
+                VectorStoreRecordDataField.builder()
+                    .withName("description")
+                    .withFieldType(String.class)
+                    .isFullTextSearchable(true).build(),
+                VectorStoreRecordVectorField.builder().withName("descriptionEmbedding")
+                    .withDimensions(4)
+                    .withIndexKind(IndexKind.HNSW)
+                    .withDistanceFunction(DistanceFunction.COSINE_DISTANCE)
+                    .withFieldType(List.class).build()));
+
+        var collection = vectorStore.getCollection("skhotels",
+            JDBCVectorStoreRecordCollectionOptions.<Hotel>builder()
+                .withRecordDefinition(hotelDefinition)
+                .withRecordClass(Hotel.class)
+                .build());
+    }
+}
diff --git a/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorsearch/hotels/Main.java b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorsearch/hotels/Main.java
new file mode 100644
index 000000000..a53562442
--- /dev/null
+++ b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorsearch/hotels/Main.java
@@ -0,0 +1,51 @@
+// Copyright (c) Microsoft. All rights reserved.
+package com.microsoft.semantickernel.samples.documentationexamples.data.vectorsearch.hotels;
+
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStore;
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreOptions;
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreRecordCollectionOptions;
+import com.microsoft.semantickernel.data.jdbc.postgres.PostgreSQLVectorStoreQueryProvider;
+import com.microsoft.semantickernel.data.vectorstorage.options.VectorSearchOptions;
+import com.microsoft.semantickernel.samples.documentationexamples.data.index.Hotel;
+import org.postgresql.ds.PGSimpleDataSource;
+import reactor.core.publisher.Mono;
+
+import java.util.List;
+
+public class Main {
+    public static void main(String[] args) {
+        // Configure the data source
+        PGSimpleDataSource dataSource = new PGSimpleDataSource();
+        dataSource.setUrl("jdbc:postgresql://localhost:5432/sk");
+        dataSource.setUser("postgres");
+        dataSource.setPassword("root");
+
+        // Create a JDBC vector store and choose an existing collection that already contains records.
+        var vectorStore = new JDBCVectorStore(dataSource, JDBCVectorStoreOptions.builder()
+            .withQueryProvider(PostgreSQLVectorStoreQueryProvider.builder()
+                .withDataSource(dataSource)
+                .build())
+            .build());
+        var collection = vectorStore.getCollection("skhotels",
+            JDBCVectorStoreRecordCollectionOptions.<Hotel>builder()
+                .withRecordClass(Hotel.class)
+                .build());
+
+        // Generate a vector for your search text, using your chosen embedding generation implementation.
+        // Just showing a placeholder method here for brevity.
+        var searchVector = generateEmbeddingsAsync(
+            "I'm looking for a hotel where customer happiness is the priority.").block();
+
+        // Do the search, passing an options object with a Top value to limit results to the single top match.
+        var searchResult = collection.searchAsync(searchVector, VectorSearchOptions.builder()
+            .withTop(1).build()).block();
+
+        // Inspect the returned hotel.
+        Hotel hotel = searchResult.getResults().get(0).getRecord();
+        System.out.printf("Found hotel description: %s\n", hotel.getDescription());
+    }
+
+    private static Mono<List<Float>> generateEmbeddingsAsync(String text) {
+        return Mono.just(List.of(1.0f, 2.0f, 3.0f, 4.0f));
+    }
+}
diff --git a/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorsearch/products/Main.java b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorsearch/products/Main.java
new file mode 100644
index 000000000..f96593284
--- /dev/null
+++ b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorsearch/products/Main.java
@@ -0,0 +1,218 @@
+// Copyright (c) Microsoft. All rights reserved.
+package com.microsoft.semantickernel.samples.documentationexamples.data.vectorsearch.products;
+
+import com.microsoft.semantickernel.data.VolatileVectorStore;
+import com.microsoft.semantickernel.data.VolatileVectorStoreRecordCollectionOptions;
+import com.microsoft.semantickernel.data.vectorsearch.VectorSearchFilter;
+import com.microsoft.semantickernel.data.vectorstorage.VectorStoreRecordCollection;
+import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordData;
+import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordKey;
+import com.microsoft.semantickernel.data.vectorstorage.annotations.VectorStoreRecordVector;
+import com.microsoft.semantickernel.data.vectorstorage.options.VectorSearchOptions;
+import reactor.core.publisher.Mono;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+
+public class Main {
+    public static void main(String[] args) {
+        // Build a query provider
+        var vectorStore = new VolatileVectorStore();
+        var collection = vectorStore.getCollection("skproducts",
+            VolatileVectorStoreRecordCollectionOptions.<Product>builder()
+                .withRecordClass(Product.class)
+                .build());
+        collection.createCollectionIfNotExistsAsync().block();
+        var vector = generateEmbeddingsAsync().block();
+        collection.upsertAsync(
+            new Product("1", "Product 1", List.of("Feature 1", "Feature 2"), vector, vector), null)
+            .block();
+
+        withVectorFieldName(collection);
+        withTopAndSkip(collection);
+        withIncludeVectors(collection);
+        withVectorSearchFilter();
+    }
+
+    public static void withVectorFieldName(
+        VectorStoreRecordCollection<String, Product> collection) {
+        // Create the vector search options and indicate that we want to search the FeatureListEmbedding field.
+        var searchOptions = VectorSearchOptions.builder()
+            .withVectorFieldName("featureListEmbedding")
+            .build();
+
+        // Generate a vector for your search text, using the embedding model of your choice
+        var searchVector = generateEmbeddingsAsync().block();
+
+        // Do the search
+        var searchResult = collection.searchAsync(searchVector, searchOptions).block();
+    }
+
+    public static void withTopAndSkip(VectorStoreRecordCollection<String, Product> collection) {
+        // Create the vector search options and indicate that we want to skip the first 40 results and then get the next 20.
+        var searchOptions = VectorSearchOptions.builder()
+            .withTop(20)
+            .withSkip(40)
+            .build();
+
+        // Generate a vector for your search text, using the embedding model of your choice
+        var searchVector = generateEmbeddingsAsync().block();
+
+        // Do the search
+        var searchResult = collection.searchAsync(searchVector, searchOptions).block();
+    }
+
+    public static void withIncludeVectors(VectorStoreRecordCollection<String, Product> collection) {
+        // Create the vector search options and indicate that we want to include vectors in the search results.
+        var searchOptions = VectorSearchOptions.builder()
+            .withIncludeVectors(true)
+            .build();
+
+        // Generate a vector for your search text, using the embedding model of your choice
+        var searchVector = generateEmbeddingsAsync().block();
+
+        // Do the search
+        var searchResult = collection.searchAsync(searchVector, searchOptions).block();
+    }
+
+    public static void withVectorSearchFilter() {
+        // Build a query provider
+        var vectorStore = new VolatileVectorStore();
+        var collection = vectorStore.getCollection("skglossary",
+            VolatileVectorStoreRecordCollectionOptions.<Glossary>builder()
+                .withRecordClass(Glossary.class)
+                .build());
+        collection.createCollectionIfNotExistsAsync().block();
+        var vector = generateEmbeddingsAsync().block();
+        collection.upsertAsync(new Glossary("1", "External Definitions", List.of("memory"),
+            "Memory", "The power of the mind to remember things", vector), null).block();
+
+        // Filter where category == 'External Definitions' and tags contain 'memory'.
+        var filter = VectorSearchFilter.builder()
+            .equalTo("category", "External Definitions")
+            .anyTagEqualTo("tags", "memory")
+            .build();
+
+        // Create the vector search options and indicate that we want to filter the search results by a specific field.
+        var searchOptions = VectorSearchOptions.builder()
+            .withVectorSearchFilter(filter)
+            .build();
+
+        // Generate a vector for your search text, using the embedding model of your choice
+        var searchVector = generateEmbeddingsAsync().block();
+
+        // Do the search
+        var searchResult = collection.searchAsync(searchVector, searchOptions).block();
+    }
+
+    public static class Product {
+        @VectorStoreRecordKey
+        private String key;
+
+        @VectorStoreRecordData
+        private String description;
+
+        @VectorStoreRecordData
+        private List<String> featureList;
+
+        @VectorStoreRecordVector(dimensions = 1536)
+        public List<Float> descriptionEmbedding;
+
+        @VectorStoreRecordVector(dimensions = 1536)
+        public List<Float> featureListEmbedding;
+
+        public Product() {
+        }
+
+        public Product(String key, String description, List<String> featureList,
+            List<Float> descriptionEmbedding, List<Float> featureListEmbedding) {
+            this.key = key;
+            this.description = description;
+            this.featureList = featureList;
+            this.descriptionEmbedding = Collections.unmodifiableList(descriptionEmbedding);
+            this.featureListEmbedding = Collections.unmodifiableList(featureListEmbedding);
+        }
+
+        public String getKey() {
+            return key;
+        }
+
+        public String getDescription() {
+            return description;
+        }
+
+        public List<String> getFeatureList() {
+            return featureList;
+        }
+
+        public List<Float> getDescriptionEmbedding() {
+            return descriptionEmbedding;
+        }
+
+        public List<Float> getFeatureListEmbedding() {
+            return featureListEmbedding;
+        }
+    }
+
+    public static class Glossary {
+        @VectorStoreRecordKey
+        private String key;
+
+        @VectorStoreRecordData(isFilterable = true)
+        private String category;
+
+        @VectorStoreRecordData(isFilterable = true)
+        private List<String> tags;
+
+        @VectorStoreRecordData
+        private String term;
+
+        @VectorStoreRecordData
+        private String definition;
+
+        @VectorStoreRecordVector(dimensions = 1536)
+        private List<Float> definitionEmbedding;
+
+        public Glossary() {
+        }
+
+        public Glossary(String key, String category, List<String> tags, String term,
+            String definition, List<Float> definitionEmbedding) {
+            this.key = key;
+            this.category = category;
+            this.tags = tags;
+            this.term = term;
+            this.definition = definition;
+            this.definitionEmbedding = Collections.unmodifiableList(definitionEmbedding);
+        }
+
+        public String getKey() {
+            return key;
+        }
+
+        public String getCategory() {
+            return category;
+        }
+
+        public List<String> getTags() {
+            return tags;
+        }
+
+        public String getTerm() {
+            return term;
+        }
+
+        public String getDefinition() {
+            return definition;
+        }
+
+        public List<Float> getDefinitionEmbedding() {
+            return definitionEmbedding;
+        }
+    }
+
+    private static Mono<List<Float>> generateEmbeddingsAsync() {
+        return Mono.just(new ArrayList<>(Collections.nCopies(1536, 1.0f)));
+    }
+}
diff --git a/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/azureaisearch/Main.java b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/azureaisearch/Main.java
new file mode 100644
index 000000000..89ce88ea3
--- /dev/null
+++ b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/azureaisearch/Main.java
@@ -0,0 +1,32 @@
+// Copyright (c) Microsoft. All rights reserved.
+package com.microsoft.semantickernel.samples.documentationexamples.data.vectorstores.azureaisearch;
+
+import com.azure.core.credential.AzureKeyCredential;
+import com.azure.search.documents.indexes.SearchIndexClientBuilder;
+import com.microsoft.semantickernel.data.azureaisearch.AzureAISearchVectorStore;
+import com.microsoft.semantickernel.data.azureaisearch.AzureAISearchVectorStoreOptions;
+import com.microsoft.semantickernel.data.azureaisearch.AzureAISearchVectorStoreRecordCollection;
+import com.microsoft.semantickernel.data.azureaisearch.AzureAISearchVectorStoreRecordCollectionOptions;
+import com.microsoft.semantickernel.samples.documentationexamples.data.index.Hotel;
+
+public class Main {
+    public static void main(String[] args) {
+        // Build the Azure AI Search client
+        var searchClient = new SearchIndexClientBuilder()
+            .endpoint("https://<your-search-service-name>.search.windows.net")
+            .credential(new AzureKeyCredential("<your-search-service-key>"))
+            .buildAsyncClient();
+
+        // Build an Azure AI Search Vector Store
+        var vectorStore = AzureAISearchVectorStore.builder()
+            .withSearchIndexAsyncClient(searchClient)
+            .withOptions(new AzureAISearchVectorStoreOptions())
+            .build();
+
+        var collection = new AzureAISearchVectorStoreRecordCollection<>(searchClient, "skhotels",
+            AzureAISearchVectorStoreRecordCollectionOptions.<Hotel>builder()
+                .withRecordClass(Hotel.class)
+                .build());
+
+    }
+}
diff --git a/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/inmemory/Main.java b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/inmemory/Main.java
new file mode 100644
index 000000000..fff6e115a
--- /dev/null
+++ b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/inmemory/Main.java
@@ -0,0 +1,19 @@
+// Copyright (c) Microsoft. All rights reserved.
+package com.microsoft.semantickernel.samples.documentationexamples.data.vectorstores.inmemory;
+
+import com.microsoft.semantickernel.data.VolatileVectorStore;
+import com.microsoft.semantickernel.data.VolatileVectorStoreRecordCollection;
+import com.microsoft.semantickernel.data.VolatileVectorStoreRecordCollectionOptions;
+import com.microsoft.semantickernel.samples.documentationexamples.data.index.Hotel;
+
+public class Main {
+    public static void main(String[] args) {
+        // Build an Azure AI Search Vector Store
+        var vectorStore = new VolatileVectorStore();
+
+        var collection = new VolatileVectorStoreRecordCollection<>("skhotels",
+            VolatileVectorStoreRecordCollectionOptions.<Hotel>builder()
+                .withRecordClass(Hotel.class)
+                .build());
+    }
+}
diff --git a/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/jdbc/Main.java b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/jdbc/Main.java
new file mode 100644
index 000000000..8b7bc1257
--- /dev/null
+++ b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/jdbc/Main.java
@@ -0,0 +1,42 @@
+// Copyright (c) Microsoft. All rights reserved.
+package com.microsoft.semantickernel.samples.documentationexamples.data.vectorstores.jdbc;
+
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStore;
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreOptions;
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreRecordCollection;
+import com.microsoft.semantickernel.data.jdbc.JDBCVectorStoreRecordCollectionOptions;
+import com.microsoft.semantickernel.data.jdbc.postgres.PostgreSQLVectorStoreQueryProvider;
+import com.microsoft.semantickernel.samples.documentationexamples.data.index.Hotel;
+import org.postgresql.ds.PGSimpleDataSource;
+
+public class Main {
+    public static void main(String[] args) {
+        // Configure the data source
+        PGSimpleDataSource dataSource = new PGSimpleDataSource();
+        dataSource.setUrl("jdbc:postgresql://localhost:5432/sk");
+        dataSource.setUser("postgres");
+        dataSource.setPassword("root");
+
+        // Build a query provider
+        // Other available query providers are MySQLVectorStoreQueryProvider, SQLiteVectorStoreQueryProvider
+        // and HSQDBVectorStoreQueryProvider
+        var queryProvider = PostgreSQLVectorStoreQueryProvider.builder()
+            .withDataSource(dataSource)
+            .build();
+
+        // Build a vector store
+        var vectorStore = JDBCVectorStore.builder()
+            .withDataSource(dataSource)
+            .withOptions(JDBCVectorStoreOptions.builder()
+                .withQueryProvider(queryProvider)
+                .build())
+            .build();
+
+        var collection = new JDBCVectorStoreRecordCollection<>(
+            dataSource,
+            "skhotels",
+            JDBCVectorStoreRecordCollectionOptions.<Hotel>builder()
+                .withRecordClass(Hotel.class)
+                .build());
+    }
+}
diff --git a/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/redis/Main.java b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/redis/Main.java
new file mode 100644
index 000000000..418c587c9
--- /dev/null
+++ b/samples/semantickernel-learn-resources/src/main/java/com/microsoft/semantickernel/samples/documentationexamples/data/vectorstores/redis/Main.java
@@ -0,0 +1,37 @@
+// Copyright (c) Microsoft. All rights reserved.
+package com.microsoft.semantickernel.samples.documentationexamples.data.vectorstores.redis;
+
+import com.microsoft.semantickernel.data.redis.RedisJsonVectorStoreRecordCollectionOptions;
+import com.microsoft.semantickernel.data.redis.RedisStorageType;
+import com.microsoft.semantickernel.data.redis.RedisVectorStore;
+import com.microsoft.semantickernel.data.redis.RedisVectorStoreOptions;
+import com.microsoft.semantickernel.samples.documentationexamples.data.index.Hotel;
+import redis.clients.jedis.JedisPooled;
+
+public class Main {
+    public static void main(String[] args) {
+        JedisPooled jedis = new JedisPooled("<your-redis-url>");
+
+        // Build a Redis Vector Store
+        // Available storage types are JSON and HASHSET. Default is JSON.
+        var vectorStore = RedisVectorStore.builder()
+            .withClient(jedis)
+            .withOptions(
+                RedisVectorStoreOptions.builder()
+                    .withStorageType(RedisStorageType.HASH_SET).build())
+            .build();
+
+        var collection = vectorStore.getCollection("skhotels",
+            RedisJsonVectorStoreRecordCollectionOptions.<Hotel>builder()
+                .withRecordClass(Hotel.class)
+                .build());
+
+        collection = vectorStore.getCollection("skhotels",
+            RedisJsonVectorStoreRecordCollectionOptions.<Hotel>builder()
+                .withRecordClass(Hotel.class)
+                .withPrefixCollectionName(false)
+                .build());
+
+        collection.getAsync("myprefix_h1", null).block();
+    }
+}