feat(redis): enhance RedisVectorStore with text search, range queries, and HNSW tuning

Add new capabilities to Redis Vector Store:
- Text search with searchByText() method and configurable scoring algorithms
- Range-based vector search with searchByRange() method
- Support for multiple distance metrics (COSINE, L2, IP)
- Configurable HNSW parameters (M, efConstruction, efRuntime)
- Document count queries with count() method
- Module-level checkstyle configuration

Update Redis documentation with comprehensive coverage of new features
including configuration properties, usage examples, and parameter guidelines.

Co-authored-by: Brian Sam-Bodden <brian@redis.com>
Signed-off-by: Mark Pollack <mark.pollack@broadcom.com>

Co-authored-by: Soby Chacko <soby.chacko@broadcom.com>

Author: markpollack

auto-configurations/vector-stores/spring-ai-autoconfigure-vector-store-redis/src/main/java/org/springframework/ai/vectorstore/redis/autoconfigure/RedisVectorStoreAutoConfiguration.java

@@ -17,11 +17,6 @@
 package org.springframework.ai.vectorstore.redis.autoconfigure;
 
 import io.micrometer.observation.ObservationRegistry;
-import redis.clients.jedis.DefaultJedisClientConfig;
-import redis.clients.jedis.HostAndPort;
-import redis.clients.jedis.JedisClientConfig;
-import redis.clients.jedis.JedisPooled;
-
 import org.springframework.ai.embedding.BatchingStrategy;
 import org.springframework.ai.embedding.EmbeddingModel;
 import org.springframework.ai.embedding.TokenCountBatchingStrategy;
@@ -38,6 +33,10 @@
 import org.springframework.boot.data.redis.autoconfigure.DataRedisAutoConfiguration;
 import org.springframework.context.annotation.Bean;
 import org.springframework.data.redis.connection.jedis.JedisConnectionFactory;
+import redis.clients.jedis.DefaultJedisClientConfig;
+import redis.clients.jedis.HostAndPort;
+import redis.clients.jedis.JedisClientConfig;
+import redis.clients.jedis.JedisPooled;
 
 /**
  * {@link AutoConfiguration Auto-configuration} for Redis Vector Store.
@@ -46,6 +45,7 @@
  * @author Eddú Meléndez
  * @author Soby Chacko
  * @author Jihoon Kim
+ * @author Brian Sam-Bodden
  */
 @AutoConfiguration(after = DataRedisAutoConfiguration.class)
 @ConditionalOnClass({ JedisPooled.class, JedisConnectionFactory.class, RedisVectorStore.class, EmbeddingModel.class })
@@ -69,14 +69,27 @@ public RedisVectorStore vectorStore(EmbeddingModel embeddingModel, RedisVectorSt
 			BatchingStrategy batchingStrategy) {
 
 		JedisPooled jedisPooled = this.jedisPooled(jedisConnectionFactory);
-		return RedisVectorStore.builder(jedisPooled, embeddingModel)
+		RedisVectorStore.Builder builder = RedisVectorStore.builder(jedisPooled, embeddingModel)
 			.initializeSchema(properties.isInitializeSchema())
 			.observationRegistry(observationRegistry.getIfUnique(() -> ObservationRegistry.NOOP))
 			.customObservationConvention(customObservationConvention.getIfAvailable(() -> null))
 			.batchingStrategy(batchingStrategy)
 			.indexName(properties.getIndexName())
-			.prefix(properties.getPrefix())
-			.build();
+			.prefix(properties.getPrefix());
+
+		// Configure HNSW parameters if available
+		hnswConfiguration(builder, properties);
+
+		return builder.build();
+	}
+
+	/**
+	 * Configures the HNSW-related parameters on the builder
+	 */
+	private void hnswConfiguration(RedisVectorStore.Builder builder, RedisVectorStoreProperties properties) {
+		builder.hnswM(properties.getHnsw().getM())
+			.hnswEfConstruction(properties.getHnsw().getEfConstruction())
+			.hnswEfRuntime(properties.getHnsw().getEfRuntime());
 	}
 
 	private JedisPooled jedisPooled(JedisConnectionFactory jedisConnectionFactory) {

auto-configurations/vector-stores/spring-ai-autoconfigure-vector-store-redis/src/main/java/org/springframework/ai/vectorstore/redis/autoconfigure/RedisVectorStoreProperties.java

@@ -18,12 +18,28 @@
 
 import org.springframework.ai.vectorstore.properties.CommonVectorStoreProperties;
 import org.springframework.boot.context.properties.ConfigurationProperties;
+import org.springframework.boot.context.properties.NestedConfigurationProperty;
 
 /**
  * Configuration properties for Redis Vector Store.
  *
+ * <p>
+ * Example application.properties:
+ * </p>
+ * <pre>
+ * spring.ai.vectorstore.redis.index-name=my-index
+ * spring.ai.vectorstore.redis.prefix=doc:
+ * spring.ai.vectorstore.redis.initialize-schema=true
+ *
+ * # HNSW algorithm configuration
+ * spring.ai.vectorstore.redis.hnsw.m=32
+ * spring.ai.vectorstore.redis.hnsw.ef-construction=100
+ * spring.ai.vectorstore.redis.hnsw.ef-runtime=50
+ * </pre>
+ *
  * @author Julien Ruaux
  * @author Eddú Meléndez
+ * @author Brian Sam-Bodden
  */
 @ConfigurationProperties(RedisVectorStoreProperties.CONFIG_PREFIX)
 public class RedisVectorStoreProperties extends CommonVectorStoreProperties {
@@ -34,6 +50,12 @@ public class RedisVectorStoreProperties extends CommonVectorStoreProperties {
 
 	private String prefix = "default:";
 
+	/**
+	 * HNSW algorithm configuration properties.
+	 */
+	@NestedConfigurationProperty
+	private HnswProperties hnsw = new HnswProperties();
+
 	public String getIndexName() {
 		return this.indexName;
 	}
@@ -50,4 +72,64 @@ public void setPrefix(String prefix) {
 		this.prefix = prefix;
 	}
 
+	public HnswProperties getHnsw() {
+		return this.hnsw;
+	}
+
+	public void setHnsw(HnswProperties hnsw) {
+		this.hnsw = hnsw;
+	}
+
+	/**
+	 * HNSW (Hierarchical Navigable Small World) algorithm configuration properties.
+	 */
+	public static class HnswProperties {
+
+		/**
+		 * M parameter for HNSW algorithm. Represents the maximum number of connections
+		 * per node in the graph. Higher values increase recall but also memory usage.
+		 * Typically between 5-100. Default: 16
+		 */
+		private Integer m = 16;
+
+		/**
+		 * EF_CONSTRUCTION parameter for HNSW algorithm. Size of the dynamic candidate
+		 * list during index building. Higher values lead to better recall but slower
+		 * indexing. Typically between 50-500. Default: 200
+		 */
+		private Integer efConstruction = 200;
+
+		/**
+		 * EF_RUNTIME parameter for HNSW algorithm. Size of the dynamic candidate list
+		 * during search. Higher values lead to more accurate but slower searches.
+		 * Typically between 20-200. Default: 10
+		 */
+		private Integer efRuntime = 10;
+
+		public Integer getM() {
+			return this.m;
+		}
+
+		public void setM(Integer m) {
+			this.m = m;
+		}
+
+		public Integer getEfConstruction() {
+			return this.efConstruction;
+		}
+
+		public void setEfConstruction(Integer efConstruction) {
+			this.efConstruction = efConstruction;
+		}
+
+		public Integer getEfRuntime() {
+			return this.efRuntime;
+		}
+
+		public void setEfRuntime(Integer efRuntime) {
+			this.efRuntime = efRuntime;
+		}
+
+	}
+
 }

auto-configurations/vector-stores/spring-ai-autoconfigure-vector-store-redis/src/test/java/org/springframework/ai/vectorstore/redis/autoconfigure/RedisVectorStoreAutoConfigurationIT.java

@@ -1,5 +1,5 @@
 /*
- * Copyright 2023-2024 the original author or authors.
+ * Copyright 2023-2025 the original author or authors.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -49,6 +49,7 @@
  * @author Soby Chacko
  * @author Christian Tzolov
  * @author Thomas Vitale
+ * @author Brian Sam-Bodden
  */
 @Testcontainers
 class RedisVectorStoreAutoConfigurationIT {
@@ -57,11 +58,13 @@ class RedisVectorStoreAutoConfigurationIT {
 	static RedisStackContainer redisContainer = new RedisStackContainer(
 			RedisStackContainer.DEFAULT_IMAGE_NAME.withTag(RedisStackContainer.DEFAULT_TAG));
 
+	// Use host and port explicitly since getRedisURI() might not be consistent
 	private final ApplicationContextRunner contextRunner = new ApplicationContextRunner()
 		.withConfiguration(
 				AutoConfigurations.of(DataRedisAutoConfiguration.class, RedisVectorStoreAutoConfiguration.class))
 		.withUserConfiguration(Config.class)
-		.withPropertyValues("spring.data.redis.url=" + redisContainer.getRedisURI())
+		.withPropertyValues("spring.data.redis.host=" + redisContainer.getHost(),
+				"spring.data.redis.port=" + redisContainer.getFirstMappedPort())
 		.withPropertyValues("spring.ai.vectorstore.redis.initialize-schema=true")
 		.withPropertyValues("spring.ai.vectorstore.redis.index=myIdx")
 		.withPropertyValues("spring.ai.vectorstore.redis.prefix=doc:")
@@ -151,4 +154,4 @@ public EmbeddingModel embeddingModel() {
 
 	}
 
-}
+}

auto-configurations/vector-stores/spring-ai-autoconfigure-vector-store-redis/src/test/java/org/springframework/ai/vectorstore/redis/autoconfigure/RedisVectorStorePropertiesTests.java

@@ -23,6 +23,7 @@
 /**
  * @author Julien Ruaux
  * @author Eddú Meléndez
+ * @author Brian Sam-Bodden
  */
 class RedisVectorStorePropertiesTests {
 
@@ -31,6 +32,11 @@ void defaultValues() {
 		var props = new RedisVectorStoreProperties();
 		assertThat(props.getIndexName()).isEqualTo("default-index");
 		assertThat(props.getPrefix()).isEqualTo("default:");
+
+		// Verify default HNSW parameters
+		assertThat(props.getHnsw().getM()).isEqualTo(16);
+		assertThat(props.getHnsw().getEfConstruction()).isEqualTo(200);
+		assertThat(props.getHnsw().getEfRuntime()).isEqualTo(10);
 	}
 
 	@Test
@@ -43,4 +49,18 @@ void customValues() {
 		assertThat(props.getPrefix()).isEqualTo("doc:");
 	}
 
+	@Test
+	void customHnswValues() {
+		var props = new RedisVectorStoreProperties();
+		RedisVectorStoreProperties.HnswProperties hnsw = props.getHnsw();
+
+		hnsw.setM(32);
+		hnsw.setEfConstruction(100);
+		hnsw.setEfRuntime(50);
+
+		assertThat(props.getHnsw().getM()).isEqualTo(32);
+		assertThat(props.getHnsw().getEfConstruction()).isEqualTo(100);
+		assertThat(props.getHnsw().getEfRuntime()).isEqualTo(50);
+	}
+
 }