Langchain4j
Langchain4j 是 langchain 库的 Java 实现。它使用类似的概念，包括提示、链、转换器、文档加载器、代理等。
Neo4j 集成使 Neo4j 向量 索引在 Langchain4j 库中可用。
安装
pom.xml
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-neo4j</artifactId>
<version>0.35.0</version>
</dependency>文档
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.onnx.allminilml6v2.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.
}
}
}