RAG with Spring AI

<dependencyManagement>

    <dependencies>

        <dependency>

            <groupId>org.springframework.ai</groupId>

            <artifactId>spring-ai-bom</artifactId>

            <version>1.0.0</version> <type>pom</type>

            <scope>import</scope>

        </dependency>

    </dependencies>

</dependencyManagement>



<dependencies>

    <dependency>

        <groupId>org.springframework.boot</groupId>

        <artifactId>spring-boot-starter-web</artifactId>

    </dependency>



    <dependency>

        <groupId>org.springframework.ai</groupId>

        <artifactId>spring-ai-openai-spring-boot-starter</artifactId>

    </dependency>

    <dependency>

        <groupId>org.springframework.ai</groupId>

        <artifactId>spring-ai-pgvector-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-starter-data-jpa</artifactId>

    </dependency>

    <dependency>

        <groupId>org.postgresql</groupId>

        <artifactId>postgresql</artifactId>

        <scope>runtime</scope>

    </dependency>

    </dependencies>

Properties

# LLM Configuration (OpenAI Example)

spring.ai.openai.api-key=${OPENAI_API_KEY}

spring.ai.openai.chat.model=gpt-4o-mini

spring.ai.openai.embedding.model=text-embedding-3-small



# PostgreSQL/PGVector Configuration

spring.datasource.url=jdbc:postgresql://localhost:5432/ragdb

spring.datasource.username=user

spring.datasource.password=password

spring.jpa.hibernate.ddl-auto=update



# Spring AI Vector Store Schema Initialization

# This creates the necessary table for the vector store

spring.ai.vectorstore.pgvector.initialize-schema=true

package com.example.ragtutorial;



import org.springframework.ai.document.Document;

import org.springframework.ai.reader.TextReader;

import org.springframework.ai.transformer.splitter.TokenTextSplitter;

import org.springframework.ai.vectorstore.VectorStore;

import org.springframework.beans.factory.annotation.Value;

import org.springframework.boot.CommandLineRunner;

import org.springframework.core.io.Resource;

import org.springframework.stereotype.Service;



import java.util.List;

import org.slf4j.Logger;

import org.slf4j.LoggerFactory;



@Service

public class IngestionService implements CommandLineRunner {



    private static final Logger log = LoggerFactory.getLogger(IngestionService.class);

    private final VectorStore vectorStore;



    // Use a text file for simplicity. Place it in src/main/resources/data/

    @Value("classpath:/data/spring-ai-info.txt")

    private Resource dataResource;



    public IngestionService(VectorStore vectorStore) {

        this.vectorStore = vectorStore;

    }



    @Override

    public void run(String... args) {

        log.info("Starting RAG document ingestion...");



        // 1. Extract: Read the document content

        TextReader textReader = new TextReader(dataResource);

        List<Document> rawDocuments = textReader.get();



        // 2. Transform: Split the large document into smaller, manageable chunks

        // TokenTextSplitter ensures chunks fit within the LLM's context window

        TokenTextSplitter textSplitter = new TokenTextSplitter();

        List<Document> splitDocuments = textSplitter.apply(rawDocuments);



        // 3. Load: Store the documents (which creates and stores embeddings)

        vectorStore.accept(splitDocuments);



        log.info("Document ingestion complete. {} chunks loaded into VectorStore.", splitDocuments.size());

    }

}

Spring AI is an application framework for AI engineering. Its goal is to apply Spring ecosystem design principles to the AI domain. It connects enterprise data and APIs with AI Models. It offers a portable API across different AI providers like OpenAI, Gemini, and Ollama. For RAG, it supports vector stores such as PGVector, Chroma, and Redis. The ChatClient API is used for communication, and the Advisor API simplifies patterns like RAG.

package com.example.ragtutorial;



import org.springframework.ai.chat.client.ChatClient;

import org.springframework.ai.vectorstore.VectorStore;

import org.springframework.ai.chat.client.advisor.QuestionAnswerAdvisor;

import org.springframework.web.bind.annotation.GetMapping;

import org.springframework.web.bind.annotation.RequestParam;

import org.springframework.web.bind.annotation.RestController;



@RestController

public class RagController {



    private final ChatClient chatClient;



    public RagController(ChatClient.Builder chatClientBuilder, VectorStore vectorStore) {

        // Configure the ChatClient with the QuestionAnswerAdvisor

        // The QuestionAnswerAdvisor handles:

        // 1. Retrieving relevant documents from the VectorStore based on the user query.

        // 2. Augmenting the user's prompt with the retrieved documents as context.

        this.chatClient = chatClientBuilder

            // This is the core of RAG implementation in Spring AI

            .defaultAdvisors(QuestionAnswerAdvisor.builder(vectorStore).build())

            .build();

    }



    @GetMapping("/rag/query")

    public String ragQuery(@RequestParam(defaultValue = "What is Spring AI and what are its features?") String query) {



        // The advisor runs before this call, injecting the retrieved context into the prompt

        return this.chatClient.prompt()

            .user(query)

            .call()

            .content();

    }

}

curl 'http://localhost:8080/rag/query?query=What is the primary goal of Spring AI?'



# Expected Output (grounded in your document): The primary goal of Spring AI is to apply Spring ecosystem design principles to the AI domain and to connect enterprise data and APIs with AI Models.