I’ve analyzed the provided AI stack and created this comprehensive, step-by-step tutorial to guide you through building a Production-Ready RAG (Retrieval-Augmented Generation) application using Java and Spring Boot.

This guide will walk you through each layer of the modern AI ecosystem shown in the diagram, from data ingestion to advanced evaluation, all within the Java environment.

Mastering RAG with Java & Spring AI: A Step-by-Step Tutorial

1. Prerequisites and Setup

To build this stack, we will use Spring AI, a new framework that brings the familiar Spring ecosystem to AI application development, eliminating the need to use Python for orchestration.

Your Stack:

• Java 17+ and Maven.
• Spring Boot 3.2.x+.
• Docker (to run a local Vector Database).

Step 1: Initialize your Spring Boot Project

Create a new Spring Boot project via Spring Initializr or your IDE. Add the required dependencies in your pom.xml. This configuration aligns with the Framework (Layer 6) from the stack diagram.

<dependencies>
    <!-- Web Support for API endpoints -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>

    <!-- Spring AI Starter - provides core abstractions -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-openai-spring-boot-starter</artifactId>
    </dependency>

    <!-- Vector DB Starter (e.g., PgVector, Qdrant, Pinecone) -->
    <!-- We'll use PgVector, which runs on PostgreSQL -->
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-pgvector-store-spring-boot-starter</artifactId>
    </dependency>

    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-test</artifactId>
        <scope>test</scope>
    </dependency>
</dependencies>

<!-- Required for Spring AI Milestones -->
<repositories>
    <repository>
        <id>spring-milestones</id>
        <name>Spring Milestones</name>
        <url>https://repo.spring.io/milestone</url>
        <snapshots>
            <enabled>false</enabled>
        </snapshots>
    </repository>
</repositories>

2. Ingestion: Storing Your Private Data

Ingestion is the foundational stage. It involves taking your unique, private data and preparing it for the AI model to access. It covers Layers 4, 3, and 2 of the AI stack.

graph TD
    A[Raw Private Data: PDFs, Docs, Websites] -->|Layer 4: Data Extraction| B(Text Extraction & Chunking)
    B -->|Layer 3: Text Embeddings| C[Text -> Vector Conversion]
    C -->|Layer 2: Vector Database| D[(Knowledge Base Storage)]

Step 2: Layer 4 — Data Extraction

To allow an LLM to search your data, you must first extract the text from its source format.

• Market Examples (from diagram): Docling, LlamaParse v2, Firecrawl, Crawl4AI.
• Java Implementation: For local files, we can use an abstraction in Spring AI that uses Apache Tika to read PDFs, Word docs, and more.

Create a service to load a directory of documents:

import org.springframework.ai.document.Document;
import org.springframework.ai.reader.tika.TikaDocumentReader;
import org.springframework.stereotype.Service;
import java.nio.file.Path;
import java.util.List;

@Service
public class IngestionService {

    public List<Document> extractTextFromFiles(Path directoryPath) {
        // Look for all PDFs in the specified directory
        TikaDocumentReader tikaReader = new TikaDocumentReader("file:" + directoryPath.toString() + "/*.pdf");
        
        // This extracts the text and creates Spring AI Document objects
        List<Document> extractedDocs = tikaReader.get();
        System.out.println("Extracted " + extractedDocs.size() + " documents from PDFs.");
        return extractedDocs;
    }
}

Step 3: Layer 3 — Text Embeddings

This is the mathematical core of RAG. A Text Embedding model takes text and converts it into a long string of numbers (a “vector”). This vector captures the meaning of the text. Sentences with similar meanings will have vectors close to each other in mathematical space.

• Market Examples (from diagram): OpenAI, Nomic, Google, Cohere.
• Java Implementation: We will use Spring AI’s EmbeddingClient interface, which connects to providers like OpenAI or local models.

No extensive coding is needed here; the VectorStore uses the EmbeddingClient automatically when we save the data.

Step 4: Layer 2 — Vector Database

Unlike a standard SQL database, a Vector Database is specialized to store these vectors and perform extremely fast “similarity searches.” When you ask a question, the database doesn’t look for matching keywords; it looks for vectors mathematically closest to the vector of your question.

• Market Examples (from diagram): Pinecone, PostgreSQL, Qdrant, Milvus, weaviate.
• Java Implementation: We’ll use PgVector, which adds vector capabilities to PostgreSQL.

Start PgVector locally using Docker:

docker run -p 5432:5432 -e POSTGRES_USER=myuser -e POSTGRES_PASSWORD=mypassword -e POSTGRES_DB=mydb ankane/pgvector

Configure your Spring application to connect to PgVector and OpenAI in application.properties:

# Vector DB Config
spring.ai.vectorstore.pgvector.schema-name=public
spring.ai.vectorstore.pgvector.table-name=vector_store
spring.ai.vectorstore.pgvector.dimension=1536 # For OpenAI embeddings
spring.datasource.url=jdbc:postgresql://localhost:5432/mydb
spring.datasource.username=myuser
spring.datasource.password=mypassword

# LLM & Embedding Config
spring.ai.openai.api-key=${OPENAI_API_KEY} # Set this in your environment

Step 5: Complete the Ingestion Pipeline

Now, update the IngestionService to combine extraction, embedding, and storage.

import org.springframework.ai.document.Document;
import org.springframework.ai.reader.tika.TikaDocumentReader;
import org.springframework.ai.transformer.splitter.TokenTextSplitter;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.stereotype.Service;
import java.nio.file.Path;
import java.util.List;

@Service
public class IngestionService {

    private final VectorStore vectorStore;

    // The vectorStore is auto-configured to use PgVector and OpenAI Embeddings
    public IngestionService(VectorStore vectorStore) {
        this.vectorStore = vectorStore;
    }

    public void ingestDirectory(Path directoryPath) {
        // 1. Data Extraction (Layer 4)
        TikaDocumentReader tikaReader = new TikaDocumentReader("file:" + directoryPath.toString() + "/*.pdf");
        List<Document> documents = tikaReader.get();

        // 2. Chunking (Critical Step for advanced RAG)
        // Models can't process massive PDFs at once. We split them into smaller, meaningful pieces.
        TokenTextSplitter splitter = new TokenTextSplitter();
        List<Document> splitDocuments = splitter.apply(documents);

        // 3. Text Embeddings & 4. Vector DB Storage (Layers 3 & 2)
        // This single line converts the text chunks into vectors AND stores them in PgVector.
        this.vectorStore.accept(splitDocuments);
        System.out.println("Stored " + splitDocuments.size() + " text chunks in the Vector DB.");
    }
}

3. Retrieval and Generation: The RAG Flow

With your knowledge base established, you can now build the application that answers questions. This involves Layers 6, 1, and 5 of the stack.

graph TD
    A[User Question] -->|Layer 3: Embed Query| B[Question Vector]
    B -->|Similarity Search| C[(Layer 2: Vector DB)]
    C -->|Retrieve Top Matches| D[Context Documents]
    D & A -->|Layer 6: Framework orchestrates| E[Constructed Prompt]
    E -->|Layer 1: LLM| F[Answer grounded in context]

Step 6: Layer 6 — Framework Orchestration (Retrieval)

The framework (Spring AI) handles the flow. It takes the user’s question, uses the VectorStore to find relevant documents, and builds the “grounded” prompt.

Step 7: Layer 1 — LLMs & Step 8: Layer 5 — Open LLM Access (Generation)

This layer provides the intelligence to generate the answer. You have options here:

• Closed Models (Layer 1): OpenAI (GPT-4), Claude, Gemini. These are accessed via API and are typically the most capable.
• Open Models (Layer 5): Llama 4, Qwen, Gemma 3, etc. These can be run locally using a tool like Ollama or accessed via high-speed providers like Groq.

Create a RagService to orchestrate this flow:

import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.prompt.PromptTemplate;
import org.springframework.ai.document.Document;
import org.springframework.ai.vectorstore.SearchRequest;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.stereotype.Service;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

@Service
public class RagService {

    private final VectorStore vectorStore;
    private final ChatClient chatClient;

    // ChatClient abstraction works for OpenAI, Llama (via Ollama), or any Layer 1/5 model.
    public RagService(VectorStore vectorStore, ChatClient.Builder chatClientBuilder) {
        this.vectorStore = vectorStore;
        this.chatClient = chatClientBuilder.build();
    }

    public String generateAnswerWithContext(String userQuestion) {
        // 1. Retrieval: Perform similarity search against the Vector DB
        // By default, this converts the user's question into a vector first.
        List<Document> similarities = this.vectorStore.similaritySearch(
                SearchRequest.query(userQuestion).withTopK(3) // Get top 3 most relevant chunks
        );

        // 2. Concatenate the retrieved text chunks to form our context
        String contextInformation = similarities.stream()
                .map(Document::getContent)
                .collect(Collectors.joining("\n\n"));

        // 3. Prompt Construction: Ground the LLM with this context
        String promptTemplate = """
                You are a precise corporate assistant. Use the provided Context section to answer the User Question.
                If the answer is not contained within the context, politely state that you do not know the answer.
                Do not make up facts.
                
                [Context Information]
                %s
                
                [User Question]
                %s
                """;

        String finalPrompt = promptTemplate.formatted(contextInformation, userQuestion);

        // 4. Generation: Call the model (Layer 1: e.g., OpenAI, Claude, Gemini)
        return chatClient.prompt().user(finalPrompt).call().content();
    }
}

Step 9: Expose the API

Create a controller to handle user interaction.

import org.springframework.web.bind.annotation.*;
import java.nio.file.Paths;

@RestController
@RequestMapping("/api/ai")
public class AiController {

    private final IngestionService ingestionService;
    private final RagService ragService;

    public AiController(IngestionService ingestionService, RagService ragService) {
        this.ingestionService = ingestionService;
        this.ragService = ragService;
    }

    // Step 1: Upload and ingest data from a directory
    @PostMapping("/ingest")
    public String ingestData(@RequestParam String path) {
        ingestionService.ingestDirectory(Paths.get(path));
        return "Ingestion complete.";
    }

    // Step 2: Ask a question based on your ingested data
    @GetMapping("/ask")
    public String ask(@RequestParam String question) {
        return ragService.generateAnswerWithContext(question);
    }
}

4. Advanced: Evaluation (Layer 7)

A basic RAG system is easy to build but hard to make reliable. Evaluation is the critical final layer for enterprise systems.

• Market Examples (from diagram): Ragas, TruLens, Giskard.

Why is this needed? A “naive” RAG system might retrieve the wrong information (bad Retrieval) or the LLM might ignore the context and make something up (Hallucination/bad Generation).

Advanced RAG requires setting up an Evaluation Pipeline where you use an “LLM-as-a-judge” to verify the outputs. For example, using a library like Ragas to generate metrics:

1. Context Precision: Did the Vector DB retrieve the exact information needed for the question?
2. Faithfulness: Is the generated answer derived only from the retrieved context?

To implement this advanced layer, you would typically integrate evaluation tools as part of your CI/CD pipeline or run continuous sampling in production to ensure answer quality is maintaining enterprise standards.