Testing is a very important aspect of Software Development, all the major frameworks provide tools or mechanisms for testing the response. Generally, Java unit testing can be done via Junit, or integration testing with the help of Mockito. The Spring AI also supports testing to ensure the Model is not producing a Hallucinated Response, but Model response testing cannot be done via the traditional way. We are storing the data in Redis Vector Store, also known as RAG and with new data or information, the AI Model response evolves with time and may result differently with each iteration hence we need to perform Spring AI Evaluation Testing of Redis Vector Store.

What is Spring AI Evaluation Testing of Redis Vector Store

The Spring AI Evaluation Testing of Redis Vector Store is the verification and validation of the response generated by the AI Model after reading embedding from Redis Vector Store. General practice is to perform Evaluation Testing is to use another AI Model to evaluate the response or the same AI Model to validate its response.

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.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;

@RestController
public class SpringAiController {

    private final ChatClient chatClient;
    @Autowired
    private VectorStore redisVectorStore;

    public SpringAiController(ChatClient.Builder builder) {
        this.chatClient = builder.build();
    }

    @GetMapping ("/redisVectorStore")
    public ChatResponse redisVectorStore(@RequestParam (value = "question", defaultValue = "What is Spring Framework?") String question) {
        return chatClient.prompt()
                .advisors(new QuestionAnswerAdvisor(redisVectorStore))
                .user(question)
                .call()
                .chatResponse();
    }
}

package com.example.springai;

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.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;

@SpringBootApplication
public class SpringAiApplication {

    @Value("vectorStore.json")
    private String vectorStoreFileName;

    @Value("classpath:/docs/spring-faq.pdf")
    private Resource pdfResource;

    @Autowired
    private VectorStore vectorStore;

    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();
        this.vectorStore.accept(tokenTextSplitter.apply(pdfReader.get()));
        return vectorStore;
    }
}

spring.application.name=SpringAi
spring.docker.compose.lifecycle-management=start-only
spring.threads.virtual.enabled=true
spring.ai.vectorstore.redis.uri=redis://localhost:6379
spring.ai.vectorstore.redis.index=redis-index
spring.ai.vectorstore.redis.prefix=redis-prefix
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=mistral
spring.ai.ollama.chat.model=mistral
spring.ai.ollama.embedding.model=mistral
# If running the Ollama Docker Instance separately, then set this property
spring.docker.compose.enabled=false

package com.example.springai;

import org.junit.jupiter.api.Test;
import org.springframework.boot.test.context.SpringBootTest;

@SpringBootTest
class SpringAiApplicationTests {

    @Test
    void contextLoads() {
    }
}

package com.example.springai.controller;

import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.QuestionAnswerAdvisor;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.evaluation.EvaluationRequest;
import org.springframework.ai.evaluation.RelevancyEvaluator;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;

import static org.junit.jupiter.api.Assertions.assertTrue;

@SpringBootTest
class SpringAiControllerTest {
    @Autowired
    VectorStore vectorStore;
    @Autowired
    SpringAiController springAiController;
    @Autowired
    private ChatModel chatModel;
    private ChatClient chatClient;

    @BeforeEach
    public void setup() {
        chatClient = ChatClient.builder(chatModel).build();
    }

    @Test
    void simpleVectorStore() {
        var userText = "What is Spring Framework?";
        var response = springAiController.redisVectorStore(userText);
        var content = response.getResult().getOutput().getText();
        var relevancyEvaluator = new RelevancyEvaluator(ChatClient.builder(chatModel));
        var evaluationRequest = new EvaluationRequest(userText, response.getMetadata()
                .get(QuestionAnswerAdvisor.RETRIEVED_DOCUMENTS), content);
        var evaluationResponse = relevancyEvaluator.evaluate(evaluationRequest);
        assertTrue(evaluationResponse.isPass(), "Response is not relevant to the question");
    }
}

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.
# 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>evaluation_testing_redis_vector_store</artifactId>
    <version>0.0.1-SNAPSHOT</version>
    <name>Evaluation Testing Redis Vector Store</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>

Run the curl to see the Spring AI Evaluation Testing of Redis Vector Store

http://localhost:8080/redisVectorStore

run the test case to see the Spring AI Evaluation Testing of Redis Vector Store