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58 lines
2.4 KiB
Markdown
58 lines
2.4 KiB
Markdown
---
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title: LangChain4j
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short_description: "Use Qdrant as the embedding store in LangChain4j to build context-aware AI applications in Java with full semantic retrieval support."
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description: "Integrate Qdrant with LangChain4j as the embedding store for Java AI apps, powering retrieval-augmented generation and semantic search over your data."
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---
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# LangChain for Java
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LangChain for Java, also known as [Langchain4J](https://github.com/langchain4j/langchain4j), is a community port of [Langchain](https://www.langchain.com/) for building context-aware AI applications in Java
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You can use Qdrant as a vector store in LangChain4j through the [`langchain4j-qdrant`](https://central.sonatype.com/artifact/dev.langchain4j/langchain4j-qdrant) module.
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## Setup
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Add the `langchain4j-qdrant` to your project dependencies.
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```xml
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-qdrant</artifactId>
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<version>VERSION</version>
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</dependency>
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```
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## Usage
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Before you use the following code sample, customize the following values for your configuration:
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- `YOUR_COLLECTION_NAME`: Use our [Collections](/documentation/manage-data/collections/) guide to create or
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list collections.
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- `YOUR_HOST_URL`: Use the GRPC URL for your system. If you used the [Quick Start](/documentation/quickstart/) guide,
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it may be http://localhost:6334. If you've deployed in the [Qdrant Cloud](/documentation/cloud/), you may have a
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longer URL such as `https://example.location.cloud.qdrant.io:6334`.
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- `YOUR_API_KEY`: Substitute the API key associated with your configuration.
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```java
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import dev.langchain4j.store.embedding.EmbeddingStore;
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import dev.langchain4j.store.embedding.qdrant.QdrantEmbeddingStore;
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EmbeddingStore<TextSegment> embeddingStore =
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QdrantEmbeddingStore.builder()
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// Ensure the collection is configured with the appropriate dimensions
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// of the embedding model.
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// Reference: https://qdrant.tech/documentation/manage-data/collections/
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.collectionName("YOUR_COLLECTION_NAME")
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.host("YOUR_HOST_URL")
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// GRPC port of the Qdrant server
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.port(6334)
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.apiKey("YOUR_API_KEY")
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.build();
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```
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`QdrantEmbeddingStore` supports all the semantic features of LangChain4j.
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## Further Reading
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- You can refer to the [LangChain4j examples](https://github.com/langchain4j/langchain4j-examples/) to get started.
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- [Source Code](https://github.com/langchain4j/langchain4j/tree/main/langchain4j-qdrant)
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