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Classification

docs/docs/tutorials/classification.md

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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.

LangChain4j supports two common approaches to text classification:

  • Use an LLM through AI Services when labels depend on nuanced natural language reasoning.
  • Use embeddings through TextClassifier and EmbeddingModelTextClassifier when you have labeled examples for each category and want to classify by semantic similarity.

Sentiment Classification Service

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

  • POSITIVE
  • NEUTRAL
  • NEGATIVE

Implementation

java
import dev.langchain4j.model.chat.ChatModel;
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 ChatModel chatModel = 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, chatModel);

        // 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

java
static ChatModel chatModel = 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

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

3. Creating the AI-Powered Sentiment Analyzer

java
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

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

5. Running Sentiment Analysis

java
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.

Embedding-Based Classification

EmbeddingModelTextClassifier classifies text by embedding the input and comparing it with embedded examples for each label. This approach can be useful when you can provide representative examples for every class and do not need an LLM call for each classification request.

java
import dev.langchain4j.classification.EmbeddingModelTextClassifier;
import dev.langchain4j.classification.TextClassifier;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.embedding.onnx.allminilml6v2q.AllMiniLmL6V2QuantizedEmbeddingModel;

import java.util.List;
import java.util.Map;

public class EmbeddingBasedSentimentClassification {

    enum Sentiment {
        POSITIVE, NEUTRAL, NEGATIVE
    }

    public static void main(String[] args) {

        Map<Sentiment, List<String>> examples = Map.of(
                Sentiment.POSITIVE, List.of("This is great!", "I love this product."),
                Sentiment.NEUTRAL, List.of("It is okay.", "This works as expected."),
                Sentiment.NEGATIVE, List.of("This is terrible.", "I am disappointed."));

        EmbeddingModel embeddingModel = new AllMiniLmL6V2QuantizedEmbeddingModel();

        TextClassifier<Sentiment> classifier = new EmbeddingModelTextClassifier<>(embeddingModel, examples);

        List<Sentiment> sentiments = classifier.classify("Awesome experience!");
        System.out.println(sentiments); // [POSITIVE]
    }
}

You can also use classifyWithScores(...) when you need the similarity score for each returned label. The classifier can return zero, one, or multiple labels depending on its maxResults, minScore, and meanToMaxScoreRatio settings.

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