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Classification

Overview​

This documentation provides an implementation of a classification system using LangChain4j in Java. Classification is essential for categorizing text into predefined labels, such as sentiment analysis, intent detection, and entity recognition.

This example demonstrates sentiment classification using LangChain4j's AI-powered services.

Sentiment Classification Service​

The sentiment classification system categorizes input text into one of the following sentiment categories:

  • POSITIVE
  • NEUTRAL
  • NEGATIVE

Implementation​

import dev.langchain4j.model.chat.ChatLanguageModel;
import dev.langchain4j.model.openai.OpenAiChatModel;
import dev.langchain4j.service.AiServices;
import dev.langchain4j.service.UserMessage;

public class SentimentClassification {

    // Initialize the chat model using OpenAI
    static ChatLanguageModel chatLanguageModel = OpenAiChatModel.withApiKey("YOUR_OPENAI_API_KEY");

    // Define the Sentiment enum
    enum Sentiment {
        POSITIVE, NEUTRAL, NEGATIVE
    }

    // Define the AI-powered Sentiment Analyzer interface
    interface SentimentAnalyzer {

        @UserMessage("Analyze sentiment of {{it}}")
        Sentiment analyzeSentimentOf(String text);

        @UserMessage("Does {{it}} have a positive sentiment?")
        boolean isPositive(String text);
    }

    public static void main(String[] args) {

        // Create an AI-powered Sentiment Analyzer instance
        SentimentAnalyzer sentimentAnalyzer = AiServices.create(SentimentAnalyzer.class, chatLanguageModel);

        // Example Sentiment Analysis
        Sentiment sentiment = sentimentAnalyzer.analyzeSentimentOf("I love this product!");
        System.out.println(sentiment); // Expected Output: POSITIVE

        boolean positive = sentimentAnalyzer.isPositive("This is a terrible experience.");
        System.out.println(positive); // Expected Output: false
    }
}

Explanation of Components​

1. Chat Model Initialization​

static ChatLanguageModel chatLanguageModel = OpenAiChatModel.withApiKey("YOUR_OPENAI_API_KEY");
  • Initializes the OpenAI Chat Model to process natural language text.
  • Replace "YOUR_OPENAI_API_KEY" with an actual OpenAI API key.

2. Defining Sentiment Categories​

enum Sentiment {
    POSITIVE, NEUTRAL, NEGATIVE
}
  • The Sentiment enum represents possible sentiment classifications.

3. Creating the AI-Powered Sentiment Analyzer​

interface SentimentAnalyzer {
    
    @UserMessage("Analyze sentiment of {{it}}")
    Sentiment analyzeSentimentOf(String text);

    @UserMessage("Does {{it}} have a positive sentiment?")
    boolean isPositive(String text);
}
  • This interface defines two AI-powered methods:
    • analyzeSentimentOf(String text): Classifies a given text as POSITIVE, NEUTRAL, or NEGATIVE.
    • isPositive(String text): Returns true if the text has a positive sentiment; otherwise, false.

4. Creating an AI Service Instance​

SentimentAnalyzer sentimentAnalyzer = AiServices.create(SentimentAnalyzer.class, chatLanguageModel);
  • AiServices.create() dynamically implements the SentimentAnalyzer interface using the AI model.

5. Running Sentiment Analysis​

Sentiment sentiment = sentimentAnalyzer.analyzeSentimentOf("I love this product!");
System.out.println(sentiment); // Output: POSITIVE

boolean positive = sentimentAnalyzer.isPositive("This is a terrible experience.");
System.out.println(positive); // Output: false
  • The AI model classifies a given text into one of the predefined sentiment categories.
  • The isPositive() method provides a boolean result.

Use Cases​

This sentiment classification service can be used in various applications, including:

✅ Customer Feedback Analysis: Classify customer reviews as positive, neutral, or negative.
✅ Social Media Monitoring: Analyze sentiment trends in social media comments.
✅ Chatbot Responses: Understand user sentiment to provide better responses.

Examples​