Langchain4j

132277850?v=4

Langchain4j is a Java implementation of the langchain library. It uses similar concepts, with Prompts, Chains, Transformers, Document Loaders, Agents, and more.

The Neo4j Integration makes the Neo4j Vector index available in the Langchain4j library.

Installation

pom.xml
        <dependency>
            <groupId>dev.langchain4j</groupId>
            <artifactId>langchain4j-neo4j</artifactId>
            <version>0.32.0</version>
        </dependency>

Functionality Includes

  • Create vector index

  • Populate nodes and vector index from documents

  • Query vector index

Documentation

import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.AllMiniLmL6V2EmbeddingModel;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingMatch;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.neo4j.Neo4jEmbeddingStore;
import org.testcontainers.containers.Neo4jContainer;

import java.util.List;

public class Neo4jEmbeddingStoreExample {

    public static void main(String[] args) {
        try (Neo4jContainer<?> neo4j = new Neo4jContainer<>("neo4j:5")) {
            neo4j.start();
            EmbeddingStore<TextSegment> embeddingStore = Neo4jEmbeddingStore.builder()
                    .withBasicAuth(neo4j.getBoltUrl(), "neo4j", neo4j.getAdminPassword())
                    .dimension(384)
                    .build();

            EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel();

            TextSegment segment1 = TextSegment.from("I like football.");
            Embedding embedding1 = embeddingModel.embed(segment1).content();
            embeddingStore.add(embedding1, segment1);

            TextSegment segment2 = TextSegment.from("The weather is good today.");
            Embedding embedding2 = embeddingModel.embed(segment2).content();
            embeddingStore.add(embedding2, segment2);

            Embedding queryEmbedding = embeddingModel.embed("What is your favourite sport?").content();
            List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.findRelevant(queryEmbedding, 1);
            EmbeddingMatch<TextSegment> embeddingMatch = relevant.get(0);

            System.out.println(embeddingMatch.score()); // 0.8144289255142212
            System.out.println(embeddingMatch.embedded().text()); // I like football.
        }
    }
}