Building an AI-Powered Application with LangChain, Java Spring Boot, and Vector Databases

Introduction

With the rise of AI-powered applications, developers are increasingly leveraging LangChain for building language-based applications, Spring Boot for backend services, and vector databases for efficient storage and retrieval of embeddings. This guide will walk through integrating these technologies into a real-world application deployed on Google Cloud Run with Firestore as a scalable database.

Prerequisites

Before getting started, ensure you have the following:

• Java 17 or later
• Spring Boot (preferably 3.x)
• A Google Cloud project with Firestore and Cloud Run enabled
• A vector database (e.g., Pinecone, Weaviate, or FAISS)
• OpenAI API key (for embedding generation using LangChain)

1. Understanding the Key Technologies

LangChain

LangChain is a powerful framework that helps build AI-driven applications using Large Language Models (LLMs). It provides tools for chaining prompts, managing memory, and handling vector-based retrieval.

Spring Boot

Spring Boot simplifies the development of Java-based microservices and REST APIs. With its extensive ecosystem, it integrates seamlessly with databases, message brokers, and cloud platforms.

Vector Databases

Vector databases store high-dimensional embeddings, allowing efficient similarity search and retrieval. Some popular choices include:

• Pinecone: Fully managed and scalable
• Weaviate: Open-source and supports hybrid search
• FAISS: Optimized for local high-speed searches

2. Setting Up a Spring Boot Project

Step 1: Initialize a Spring Boot Project

Use Spring Initializr or create a pom.xml with necessary dependencies:

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>com.google.cloud</groupId>
        <artifactId>google-cloud-firestore</artifactId>
        <version>2.7.0</version>
    </dependency>
    <dependency>
        <groupId>dev.langchain4j</groupId>
        <artifactId>langchain4j</artifactId>
        <version>latest</version>
    </dependency>
</dependencies>

Step 2: Configure Firestore

1. Enable Firestore in Google Cloud Console.
2. Download the service account JSON key and set it in application.properties:

• spring.cloud.gcp.firestore.credentials.location=file:/path/to/your-service-account.json

1. Create a Firestore service:

• @Service public class FirestoreService { private final Firestore firestore; @Autowired public FirestoreService(Firestore firestore) { this.firestore = firestore; } public void saveData(String collection, String documentId, Map<String, Object> data) { firestore.collection(collection).document(documentId).set(data); } }

3. Integrating LangChain for Text Embeddings

Step 1: Generate Embeddings

import dev.langchain4j.model.openai.OpenAiEmbeddingModel;
import java.util.List;

public class EmbeddingService {
    private final OpenAiEmbeddingModel embeddingModel;
    public EmbeddingService(String openAiApiKey) {
        this.embeddingModel = new OpenAiEmbeddingModel(openAiApiKey);
    }
    public List<Float> generateEmbedding(String text) {
        return embeddingModel.embed(text).vector();
    }
}

Step 2: Store Embeddings in Firestore

public void storeEmbedding(String id, String text) {
    List<Float> embedding = generateEmbedding(text);
    Map<String, Object> docData = Map.of("embedding", embedding, "text", text);
    firestoreService.saveData("embeddings", id, docData);
}

4. Integrating a Vector Database

To perform similarity search, store embeddings in Pinecone or Weaviate.

Step 1: Set Up Pinecone Client

import io.pinecone.Pinecone;

public class PineconeService {
    private final Pinecone pinecone;
    public PineconeService(String apiKey, String indexName) {
        this.pinecone = new Pinecone(apiKey);
    }
    public void insertVector(String id, List<Float> vector) {
        pinecone.upsert(id, vector);
    }
}

Step 2: Perform Similarity Search

public List<String> searchSimilar(String query) {
    List<Float> queryEmbedding = generateEmbedding(query);
    return pinecone.query(queryEmbedding, 5); // Retrieve top 5 similar vectors
}

5. Deploying to Cloud Run

Step 1: Build a Docker Image

Create a Dockerfile:

FROM openjdk:17-jdk-slim
COPY target/app.jar app.jar
ENTRYPOINT ["java", "-jar", "app.jar"]

Step 2: Deploy

gcloud builds submit --tag gcr.io/YOUR_PROJECT_ID/app
gcloud run deploy --image gcr.io/YOUR_PROJECT_ID/app --platform managed

Conclusion

By combining LangChain, Java Spring Boot, and Vector Databases, we can build intelligent applications capable of processing and retrieving text efficiently. Firestore provides scalable storage, while Cloud Run ensures smooth deployment.

This setup is ideal for AI-powered search applications, recommendation systems, and chatbots.