landing_page/qdrant-landing/content/documentation/frameworks/langchain4j.md

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LangChain4j

LangChain for Java

LangChain for Java, also known as Langchain4J, is a community port of Langchain for building context-aware AI applications in Java

You can use Qdrant as a vector store in LangChain4j through the langchain4j-qdrant module.

Setup

Add the langchain4j-qdrant to your project dependencies.

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-qdrant</artifactId>
    <version>VERSION</version>
</dependency>

Usage

Before you use the following code sample, customize the following values for your configuration:

• YOUR_COLLECTION_NAME: Use our Collections guide to create or list collections.
• YOUR_HOST_URL: Use the GRPC URL for your system. If you used the Quick Start guide, it may be http://localhost:6334. If you've deployed in the Qdrant Cloud, you may have a longer URL such as https://example.location.cloud.qdrant.io:6334.
• YOUR_API_KEY: Substitute the API key associated with your configuration.

import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.qdrant.QdrantEmbeddingStore;

EmbeddingStore<TextSegment> embeddingStore =
    QdrantEmbeddingStore.builder()
        // Ensure the collection is configured with the appropriate dimensions
        // of the embedding model.
        // Reference: https://qdrant.tech/documentation/manage-data/collections/
        .collectionName("YOUR_COLLECTION_NAME")
        .host("YOUR_HOST_URL")
        // GRPC port of the Qdrant server
        .port(6334)
        .apiKey("YOUR_API_KEY")
        .build();

QdrantEmbeddingStore supports all the semantic features of LangChain4j.

Further Reading

• You can refer to the LangChain4j examples to get started.
• Source Code