The Spring Ai Redis Vector Store is the Redis-based vector store implementation of VectorStore interface. The Spring Ai Redis Vector Store stores vector embeddings and metadata into Redis JSON documents, and for searching It uses RediSearch for creating and querying vector embeddings for similarity indexes. Spring Ai Redis Vector Store with Jedis allows additional Redis configurations.
Jedis is a lightweight Java client library that allows applications to communicate with Redis servers. It is performant and can still handle large amounts of memory.
Lettuce is a more advanced Redis client than Jedis and supports asynchronous and reactive connections
package com.example.springai.controller;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.QuestionAnswerAdvisor;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.document.Document;
import org.springframework.ai.vectorstore.SearchRequest;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import java.util.List;
@RestController
public class SpringAiController {
private final ChatClient chatClient;
@Autowired
private VectorStore redisVectorStore;
public SpringAiController(ChatClient.Builder builder) {
this.chatClient = builder.build();
}
@GetMapping("/redisVectorStore")
public String redisVectorStore(@RequestParam(value = "question", defaultValue = "What is Spring Framework?") String question) {
ChatResponse response = chatClient.prompt()
.advisors(new QuestionAnswerAdvisor(redisVectorStore))
.user(question)
.call()
.chatResponse();
return response.getResult().getOutput().getContent();
}
@GetMapping("/redisVectorStoreSimilaritySearch")
public List<Document> redisVectorStoreSimilaritySearch(@RequestParam(value = "question", defaultValue = "What is Spring Framework?") String question) {
List<Document> results = redisVectorStore.similaritySearch(SearchRequest.builder()
.query(question)
.topK(5)
.similarityThresholdAll()
.build());
return results;
}
}
package com.example.springai;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.reader.ExtractedTextFormatter;
import org.springframework.ai.reader.pdf.PagePdfDocumentReader;
import org.springframework.ai.reader.pdf.config.PdfDocumentReaderConfig;
import org.springframework.ai.transformer.splitter.TokenTextSplitter;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.ai.vectorstore.redis.RedisVectorStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import org.springframework.core.io.Resource;
import redis.clients.jedis.JedisPooled;
@SpringBootApplication
public class SpringAiApplication {
@Value("classpath:/docs/spring-faq.pdf")
private Resource pdfResource;
@Value("${spring.ai.vectorstore.redis.port}")
private int redisPort;
@Value("${spring.ai.vectorstore.redis.uri}")
private String redisUri;
@Value("${spring.ai.vectorstore.redis.index}")
private String redisIndex;
@Value("${spring.ai.vectorstore.redis.prefix}")
private String redisPrefix;
@Value("${spring.ai.vectorstore.redis.initialize-schema}")
private boolean redisInitializeSchema;
@Autowired
private VectorStore vectorStore;
@Autowired
private EmbeddingModel embeddingModel;
public static void main(String[] args) {
SpringApplication.run(SpringAiApplication.class, args);
}
@Bean
VectorStore redisVectorStore() {
System.out.println("SpringAiApplication.redisVectorStore");
PagePdfDocumentReader pdfReader = new PagePdfDocumentReader(pdfResource, PdfDocumentReaderConfig.builder()
.withPageExtractedTextFormatter(ExtractedTextFormatter.builder()
.withNumberOfBottomTextLinesToDelete(3)
.withNumberOfTopPagesToSkipBeforeDelete(1)
.build())
.withPagesPerDocument(1)
.build());
var tokenTextSplitter = new TokenTextSplitter();
RedisVectorStore vectorStore = RedisVectorStore.builder(jedisPooled(), embeddingModel)
.indexName(redisIndex)
.prefix(redisPrefix)
.initializeSchema(redisInitializeSchema)
.vectorAlgorithm(RedisVectorStore.Algorithm.HSNW)
.build();
vectorStore.accept(tokenTextSplitter.apply(pdfReader.get()));
return vectorStore;
}
@Bean
public JedisPooled jedisPooled() {
JedisPooled jedisPooled = new JedisPooled(redisUri, redisPort);
return jedisPooled;
}
}
spring.application.name=SpringAi
spring.docker.compose.lifecycle-management=start-only
spring.threads.virtual.enabled=true
spring.ai.vectorstore.redis.uri=localhost
spring.ai.vectorstore.redis.port=6379
spring.ai.vectorstore.redis.index=spring-ai-redis-index
spring.ai.vectorstore.redis.prefix=spring-ai-redis-embedding
spring.ai.vectorstore.redis.initialize-schema=true
# The default Ollama Model in Spring Ai is mistral, but it can be changed by setting the below property. make sure to download the same model in entrypoint.sh file
#spring.ai.ollama.chat.options.model=llama3.1
spring.ai.ollama.embedding.model=mistral
# If running the Ollama Docker Instance separately, then set this property
spring.docker.compose.enabled=false
services:
ollama-model:
image: ollama/ollama:latest
container_name: ollama_container
ports:
- 11434:11434/tcp
healthcheck:
test: ollama --version || exit 1
command: serve
volumes:
- ./ollama/ollama:/root/.ollama
- ./entrypoint.sh:/entrypoint.sh
pull_policy: missing
tty: true
restart: no
entrypoint: [ "/usr/bin/bash", "/entrypoint.sh" ]
open-webui:
image: ghcr.io/open-webui/open-webui:main
container_name: open_webui_container
environment:
WEBUI_AUTH: false
ports:
- "8081:8080"
extra_hosts:
- "host.docker.internal:host-gateway"
volumes:
- open-webui:/app/backend/data
restart: no
redis:
image: redislabs/redismod
container_name: redislabs_container
restart: always
ports:
- "6379:6379"
# in redis-insight ui, add host name as redis
redis-insight:
image: redis/redisinsight:latest
container_name: redis_insight_container
restart: always
ports:
- '5540:5540'
volumes:
- redisinsight_db:/db
volumes:
open-webui:
redisinsight_db:
#!/bin/bash
# Start Ollama in the background.
/bin/ollama serve &
# Record Process ID.
pid=$!
# Pause for Ollama to start.
sleep 5
# The default Ollama Model in Spring Ai is mistral, but it can be changed in the applications property file. Make sure to download the same Model here
echo "🔴 Retrieve LLAMA3 model..."
ollama pull mistral
echo "🟢 Done!"
# Wait for the Ollama process to finish.
wait $pid
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://maven.apache.org/POM/4.0.0"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<parent>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-parent</artifactId>
<version>3.3.2</version>
<relativePath/>
</parent>
<groupId>com.example.springai</groupId>
<artifactId>redis-vector-store-with-jedis</artifactId>
<version>0.0.1-SNAPSHOT</version>
<name>Redis Vector Store With Jedis</name>
<description>Demo project for Spring Boot</description>
<properties>
<java.version>21</java.version>
<spring-ai.version>1.0.0-SNAPSHOT</spring-ai.version>
</properties>
<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-redis-store-spring-boot-starter</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-pdf-document-reader</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-docker-compose</artifactId>
<scope>runtime</scope>
<optional>true</optional>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
</dependencies>
<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-bom</artifactId>
<version>${spring-ai.version}</version>
<type>pom</type>
<scope>import</scope>
</dependency>
</dependencies>
</dependencyManagement>
<build>
<plugins>
<plugin>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-maven-plugin</artifactId>
<configuration>
<mainClass>com.example.springai.SpringAiApplication</mainClass>
<excludes>
<exclude>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
</exclude>
</excludes>
</configuration>
</plugin>
</plugins>
</build>
<repositories>
<repository>
<id>spring-milestones</id>
<name>Spring Milestones</name>
<url>https://repo.spring.io/milestone</url>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
<repository>
<id>spring-snapshots</id>
<name>Spring Snapshots</name>
<url>https://repo.spring.io/snapshot</url>
<releases>
<enabled>false</enabled>
</releases>
</repository>
</repositories>
</project>
Open http://localhost:5540/ to log in to Redis Insight, add the hostname as Redis, and leave the username and password blank.
Run the curl to see the Spring Ai Redis Vector Store with Jedis
curl --location 'localhost:8080/redisVectorStore'
curl --location 'localhost:8080/redisVectorStoreSimilaritySearch'
