Add support for Redis vector store

pom.xml

@@ -30,6 +30,7 @@
 		<module>spring-ai-spring-boot-starters/spring-ai-starter-pinecone-store</module>
 		<module>spring-ai-spring-boot-starters/spring-ai-starter-azure-store</module>
 		<module>spring-ai-spring-boot-starters/spring-ai-starter-weaviate-store</module>
+		<module>spring-ai-spring-boot-starters/spring-ai-starter-redis</module>
 		<module>spring-ai-docs</module>
 		<module>vector-stores/spring-ai-pgvector-store</module>
 		<module>vector-stores/spring-ai-milvus-store</module>
@@ -42,6 +43,7 @@
 		<module>vector-stores/spring-ai-chroma</module>
 		<module>vector-stores/spring-ai-azure</module>
 		<module>vector-stores/spring-ai-weaviate</module>
+		<module>vector-stores/spring-ai-redis</module>
 		<module>spring-ai-vertex-ai</module>
 		<module>spring-ai-spring-boot-starters/spring-ai-starter-vertex-ai</module>
 

spring-ai-docs/src/main/antora/modules/ROOT/nav.adoc

@@ -16,6 +16,7 @@
 *** xref:api/vectordbs/neo4j.adoc[]
 *** xref:api/vectordbs/pgvector.adoc[]
 *** xref:api/vectordbs/weaviate.adoc[]
+*** xref:api/vectordbs/redis.adoc[]
 ** xref:api/testing.adoc[]
 * Appendices
 ** xref:glossary.adoc[]

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/etl-pipeline.adoc

@@ -93,6 +93,10 @@ public interface DocumentWriter extends Consumer<List<Document>> {
 *Neo4jVectorStore*::
 + Leverages the Neo4j graph database for vector storage.
 
+*RedisVectorStore*::
++ Provides vector storage capabilities using Redis.
+
+
 == Using PDF Reader
 
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs.adoc

@@ -94,6 +94,7 @@ These are the available implementations of the `VectorStore` interface:
 * Neo4j [`Neo4jVectorStore`]: The https://neo4j.com/[Neo4j] vector store
 * Weaviate [`WeaviateVectorStore`] The https://weaviate.io/[Weaviate] vector store
 * Azure Vector Search [`AzureVectorStore`] the https://learn.microsoft.com/en-us/azure/search/vector-search-overview[Azure] vector store
+* Redisj [`RedisVectorStore`]: The https://redis.io/[Redis] vector store
 
 More implementations may be supported in future releases.
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/redis.adoc

@@ -0,0 +1,183 @@
+= Redis
+
+This section walks you through setting up `RedisVectorStore` to store document embeddings and perform similarity searches.
+
+== What is Redis?
+
+link:https://redis.io[Redis] is an open source (BSD licensed), in-memory data structure store used as a database, cache, message broker, and streaming engine. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
+
+== What is Redis Vector Search?
+
+link:https://redis.io/docs/interact/search-and-query/[Redis Search and Query] extends the core features of Redis OSS and allows you to use Redis as a vector database:
+
+* Store vectors and the associated metadata within hashes or JSON documents
+* Retrieve vectors
+* Perform vector searches
+
+== Prerequisites
+
+1. `EmbeddingClient` instance to compute the document embeddings. Several options are available:
+
+- `Transformers Embedding` - computes the embedding in your local environment. Follow the ONNX Transformers Embedding instructions.
+- `OpenAI Embedding` - uses the OpenAI embedding endpoint. You need to create an account at link:https://platform.openai.com/signup[OpenAI Signup] and generate the api-key token at link:https://platform.openai.com/account/api-keys[API Keys].
+- You can also use the `Azure OpenAI Embedding`.
+
+2. A Redis Stack instance
+a. https://app.redislabs.com/#/[Redis Cloud] (recommended)
+b. link:https://hub.docker.com/r/redis/redis-stack[Docker] image _redis/redis-stack:latest_
+
+
+== Dependencies
+
+Add these dependencies to your project:
+
+* Embedding Client boot starter, required for calculating embeddings.
+
+* Transformers Embedding (Local) and follow the ONNX Transformers Embedding instructions.
+
+[source,xml]
+----
+<dependency>
+  <groupId>org.springframework.ai</groupId>
+  <artifactId>spring-ai-transformers-embedding-spring-boot-starter</artifactId>
+  <version>0.8.0-SNAPSHOT</version>
+</dependency>
+----
+
+or use OpenAI (Cloud)
+
+[source,xml]
+----
+<dependency>
+    <groupId>org.springframework.ai</groupId>
+    <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
+    <version>0.8.0-SNAPSHOT</version>
+</dependency>
+----
+
+You'll need to provide your OpenAI API Key. Set it as an environment variable like so:
+
+[source,bash]
+----
+export SPRING_AI_OPENAI_API_KEY='Your_OpenAI_API_Key'
+----
+
+* Add the Redis Vector Store and Jedis dependencies
+
+[source,xml]
+----
+<dependency>
+  <groupId>org.springframework.ai</groupId>
+  <artifactId>spring-ai-redis-store</artifactId>
+  <version>0.8.0-SNAPSHOT</version>
+</dependency>
+
+<dependency>
+    <groupId>redis.clients</groupId>
+    <artifactId>jedis</artifactId>
+    <version>5.1.0</version>
+</dependency>
+----
+
+== Usage
+
+Create a RedisVectorStore instance connected to your Redis database:
+
+[source,java]
+----
+@Bean
+public VectorStore vectorStore(EmbeddingClient embeddingClient) {
+  RedisVectorStoreConfig config = RedisVectorStoreConfig.builder()
+     .withURI("redis://localhost:6379")
+     // Define the metadata fields to be used
+     // in the similarity search filters.
+     .withMetadataFields(
+        MetadataField.tag("country"),
+        MetadataField.numeric("year"))
+     .build();
+
+  return new RedisVectorStore(config, embeddingClient);
+}
+----
+
+> [NOTE]
+> You must list explicitly all metadata field names and types (`TAG`, `TEXT`, or `NUMERIC`) for any metadata field used in filter expression.
+> The `withMetadataFields` above registers filterable metadata fields: `country` of type `TAG`, `year` of type `NUMERIC`.
+>
+
+Then in your main code, create some documents:
+
+[source,java]
+----
+List<Document> documents = List.of(
+   new Document("Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!! Spring AI rocks!!", Map.of("country", "UK", "year", 2020)),
+   new Document("The World is Big and Salvation Lurks Around the Corner", Map.of()),
+   new Document("You walk forward facing the past and you turn back toward the future.", Map.of("country", "NL", "year", 2023)));
+----
+
+Now add the documents to your vector store:
+
+
+[source,java]
+----
+vectorStore.add(List.of(document));
+----
+
+And finally, retrieve documents similar to a query:
+
+[source,java]
+----
+List<Document> results = vectorStore.similaritySearch(
+   SearchRequest
+      .query("Spring")
+      .withTopK(5));
+----
+
+If all goes well, you should retrieve the document containing the text "Spring AI rocks!!".
+
+=== Metadata filtering
+
+You can leverage the generic, portable link:https://docs.spring.io/spring-ai/reference/api/vectordbs.html#_metadata_filters[metadata filters] with RedisVectorStore as well.
+
+For example, you can use either the text expression language:
+
+[source,java]
+----
+vectorStore.similaritySearch(
+   SearchRequest
+      .query("The World")
+      .withTopK(TOP_K)
+      .withSimilarityThreshold(SIMILARITY_THRESHOLD)
+      .withFilterExpression("country in ['UK', 'NL'] && year >= 2020"));
+----
+
+or programmatically using the expression DSL:
+
+[source,java]
+----
+FilterExpressionBuilder b = Filter.builder();
+
+vectorStore.similaritySearch(
+   SearchRequest
+      .query("The World")
+      .withTopK(TOP_K)
+      .withSimilarityThreshold(SIMILARITY_THRESHOLD)
+      .withFilterExpression(b.and(
+         b.in("country", "UK", "NL"),
+         b.gte("year", 2020)).build()));
+----
+
+The portable filter expressions get automatically converted into link:https://redis.io/docs/interact/search-and-query/query/[Redis search queries].
+For example, the following portable filter expression:
+
+[source,sql]
+----
+country in ['UK', 'NL'] && year >= 2020
+----
+
+is converted into Redis query:
+
+[source]
+----
+@country:{UK | NL} @year:[2020 inf]
+----

spring-ai-spring-boot-autoconfigure/pom.xml

@@ -132,6 +132,21 @@
 			<version>${project.parent.version}</version>
 			<optional>true</optional>
 		</dependency>
+
+		<!-- Redis Vector Store-->
+		<dependency>
+			<groupId>org.springframework.ai</groupId>
+			<artifactId>spring-ai-redis</artifactId>
+			<version>${project.parent.version}</version>
+			<optional>true</optional>
+		</dependency>
+
+		<!-- Override Jedis version -->
+		<dependency>
+			<groupId>redis.clients</groupId>
+			<artifactId>jedis</artifactId>
+			<version>5.1.0</version>
+		</dependency>		
 
 		<!-- Vertex AI LLM -->
 		<dependency>
@@ -186,6 +201,13 @@
 			<version>${testcontainers.version}</version>
 			<scope>test</scope>
 		</dependency>
+
+		<dependency>
+			<groupId>com.redis</groupId>
+			<artifactId>testcontainers-redis</artifactId>
+			<version>2.0.1</version>
+			<scope>test</scope>
+		</dependency>
 
 		<dependency>
 			<groupId>org.awaitility</groupId>

spring-ai-spring-boot-autoconfigure/src/main/java/org/springframework/ai/autoconfigure/vectorstore/redis/RedisVectorStoreAutoConfiguration.java

@@ -0,0 +1,50 @@
+/*
+ * Copyright 2023-2023 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.
+ * You may obtain a copy of the License at
+ *
+ *      https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.springframework.ai.autoconfigure.vectorstore.redis;
+
+import org.springframework.ai.embedding.EmbeddingClient;
+import org.springframework.ai.vectorstore.RedisVectorStore;
+import org.springframework.ai.vectorstore.RedisVectorStore.RedisVectorStoreConfig;
+import org.springframework.ai.vectorstore.VectorStore;
+import org.springframework.boot.autoconfigure.AutoConfiguration;
+import org.springframework.boot.autoconfigure.condition.ConditionalOnClass;
+import org.springframework.boot.autoconfigure.condition.ConditionalOnMissingBean;
+import org.springframework.boot.context.properties.EnableConfigurationProperties;
+import org.springframework.context.annotation.Bean;
+
+/**
+ * @author Christian Tzolov
+ */
+@AutoConfiguration
+@ConditionalOnClass({ RedisVectorStore.class, EmbeddingClient.class })
+@EnableConfigurationProperties(RedisVectorStoreProperties.class)
+public class RedisVectorStoreAutoConfiguration {
+
+	@Bean
+	@ConditionalOnMissingBean
+	public VectorStore vectorStore(EmbeddingClient embeddingClient, RedisVectorStoreProperties properties) {
+
+		var config = RedisVectorStoreConfig.builder()
+			.withURI(properties.getUri())
+			.withIndexName(properties.getIndex())
+			.withPrefix(properties.getPrefix())
+			.build();
+
+		return new RedisVectorStore(config, embeddingClient);
+	}
+
+}

spring-ai-spring-boot-autoconfigure/src/main/java/org/springframework/ai/autoconfigure/vectorstore/redis/RedisVectorStoreProperties.java

@@ -0,0 +1,61 @@
+/*
+ * Copyright 2023-2023 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.
+ * You may obtain a copy of the License at
+ *
+ *      https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.springframework.ai.autoconfigure.vectorstore.redis;
+
+import org.springframework.boot.context.properties.ConfigurationProperties;
+
+import static org.springframework.ai.autoconfigure.vectorstore.redis.RedisVectorStoreProperties.CONFIG_PREFIX;
+
+/**
+ * @author Julien Ruaux
+ */
+@ConfigurationProperties(CONFIG_PREFIX)
+public class RedisVectorStoreProperties {
+
+	public static final String CONFIG_PREFIX = "spring.ai.vectorstore.redis";
+
+	private String uri = "redis://localhost:6379";
+
+	private String index;
+
+	private String prefix;
+
+	public String getUri() {
+		return uri;
+	}
+
+	public void setUri(String uri) {
+		this.uri = uri;
+	}
+
+	public String getIndex() {
+		return index;
+	}
+
+	public void setIndex(String name) {
+		this.index = name;
+	}
+
+	public String getPrefix() {
+		return prefix;
+	}
+
+	public void setPrefix(String prefix) {
+		this.prefix = prefix;
+	}
+
+}

spring-ai-spring-boot-autoconfigure/src/main/resources/META-INF/spring/org.springframework.boot.autoconfigure.AutoConfiguration.imports

@@ -4,6 +4,7 @@ org.springframework.ai.autoconfigure.azure.openai.AzureOpenAiAutoConfiguration
 org.springframework.ai.autoconfigure.vectorstore.pgvector.PgVectorStoreAutoConfiguration
 org.springframework.ai.autoconfigure.vectorstore.pinecone.PineconeVectorStoreAutoConfiguration
 org.springframework.ai.autoconfigure.vectorstore.milvus.MilvusVectorStoreAutoConfiguration
+org.springframework.ai.autoconfigure.vectorstore.redis.RedisVectorStoreAutoConfiguration
 org.springframework.ai.autoconfigure.embedding.transformer.TransformersEmbeddingClientAutoConfiguration
 org.springframework.ai.autoconfigure.huggingface.HuggingfaceAutoConfiguration
 org.springframework.ai.autoconfigure.vectorstore.chroma.ChromaVectorStoreAutoConfiguration