Implementing Spring Boot Semantic Search with Embeddings

Semantic search is a cutting-edge technology that enables search systems to understand the context and intent behind a search query, providing more accurate and relevant results. In this tutorial, we will explore how to implement semantic search with embeddings in Spring Boot, leveraging the power of artificial intelligence (AI) to enhance search functionality.

Introduction to Semantic Search

Semantic search is a type of search that focuses on the meaning and context of the search query, rather than just keyword matching. This approach allows search systems to better understand the user’s intent and provide more relevant results. To achieve this, semantic search relies on natural language processing (NLP) and machine learning techniques, such as embeddings.

Embeddings are a way to represent words, phrases, or documents as vectors in a high-dimensional space, where similar items are closer together. This enables search systems to capture the semantic relationships between different pieces of content and provide more accurate search results.

Prerequisites

Before diving into the implementation, make sure you have the following prerequisites:

• Java 11 or later installed on your system
• Spring Boot 2.5 or later installed on your system
• Familiarity with Java Algorithms and data structures
• Basic understanding of SQL and database concepts

Step 1: Setting Up the Project

To start, create a new Spring Boot project using your preferred IDE or the Spring Initializr web tool. Add the following dependencies to your pom.xml file:

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-data-jpa</artifactId>
    </dependency>
    <dependency>
        <groupId>com.h2database</groupId>
        <artifactId>h2</artifactId>
    </dependency>
</dependencies>

These dependencies include Spring Web, Spring Data JPA, and H2 database.

Step 2: Configuring the Database

Next, configure the database connection properties in the application.properties file:

spring.datasource.url=jdbc:h2:mem:semantic-search
spring.datasource.driverClassName=org.h2.Driver
spring.datasource.username=sa
spring.datasource.password=
spring.jpa.database-platform=org.hibernate.dialect.H2Dialect

This configuration sets up an in-memory H2 database for development purposes.

Step 3: Creating the Entity Model

Now, create an entity model to represent the data that will be searched:

@Entity
public class Document {
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    private Long id;
    private String title;
    private String content;

    // Getters and setters
}

This entity model represents a document with an ID, title, and content.

Step 4: Implementing Semantic Search with Embeddings

To implement semantic search with embeddings, you will need to use a library such as Sentence-BERT (sbert). Add the following dependency to your pom.xml file:

<dependency>
    <groupId>com.github.ukplu</groupId>
    <artifactId>sbert</artifactId>
</dependency>

Then, create a service class to handle the semantic search:

@Service
public class SemanticSearchService {
    @Autowired
    private DocumentRepository documentRepository;

    public List<Document> search(String query) {
        // Use sbert to generate embeddings for the query and documents
        List<Document> documents = documentRepository.findAll();
        List<Document> results = new ArrayList<>();

        for (Document document : documents) {
            // Calculate the similarity between the query and document embeddings
            float similarity = calculateSimilarity(query, document.getContent());

            if (similarity > 0.5) {
                results.add(document);
            }
        }

        return results;
    }

    private float calculateSimilarity(String query, String document) {
        // Use sbert to generate embeddings for the query and document
        float[] queryEmbedding = Sbert.INSTANCE.encode(query);
        float[] documentEmbedding = Sbert.INSTANCE.encode(document);

        // Calculate the cosine similarity between the embeddings
        return cosineSimilarity(queryEmbedding, documentEmbedding);
    }

    private float cosineSimilarity(float[] vector1, float[] vector2) {
        // Calculate the dot product of the vectors
        float dotProduct = 0;
        for (int i = 0; i < vector1.length; i++) {
            dotProduct += vector1[i] * vector2[i];
        }

        // Calculate the magnitude of the vectors
        float magnitude1 = 0;
        for (int i = 0; i < vector1.length; i++) {
            magnitude1 += vector1[i] * vector1[i];
        }
        magnitude1 = (float) Math.sqrt(magnitude1);

        float magnitude2 = 0;
        for (int i = 0; i < vector2.length; i++) {
            magnitude2 += vector2[i] * vector2[i];
        }
        magnitude2 = (float) Math.sqrt(magnitude2);

        // Calculate the cosine similarity
        return dotProduct / (magnitude1 * magnitude2);
    }
}

This service class uses sbert to generate embeddings for the query and documents, and then calculates the similarity between the embeddings using the cosine similarity metric.

Step 5: Integrating with Spring Boot

Finally, integrate the semantic search service with Spring Boot by creating a REST controller:

@RestController
public class SearchController {
    @Autowired
    private SemanticSearchService semanticSearchService;

    @GetMapping("/search")
    public List<Document> search(@RequestParam String query) {
        return semanticSearchService.search(query);
    }
}

This controller exposes a REST endpoint that accepts a search query as a parameter and returns a list of relevant documents.

Common Mistakes and Troubleshooting

When implementing semantic search with embeddings, there are several common mistakes to watch out for:

• Insufficient training data: Make sure you have a large enough dataset to train your embeddings model.
• Poor embedding quality: Use a high-quality embeddings model that is suitable for your specific use case.
• Inadequate similarity metric: Choose a suitable similarity metric, such as cosine similarity, to compare the embeddings.

For more information on Spring Boot Tutorials, check out our other tutorials. Additionally, you can learn more about Spring Batch Guide and More Java Tutorials.

Conclusion

In this tutorial, we have implemented semantic search with embeddings in Spring Boot, using a combination of natural language processing and machine learning techniques. By following these steps and avoiding common mistakes, you can create a powerful search system that provides accurate and relevant results for your users. Remember to check out our other tutorials on SOLID Design Principles in Java and Java Interview Questions for more information on Java and Spring Boot development.