This project uses Quarkus, the Supersonic Subatomic Java Framework.
If you want to learn more about Quarkus, please visit its website: https://quarkus.io/.
You can run your application in dev mode that enables live coding using:
./mvnw quarkus:devNOTE: Quarkus now ships with a Dev UI, which is available in dev mode only at http://localhost:8080/q/dev/.
The application can be packaged using:
./mvnw packageIt produces the quarkus-run.jar file in the target/quarkus-app/ directory.
Be aware that it’s not an über-jar as the dependencies are copied into the target/quarkus-app/lib/ directory.
The application is now runnable using java -jar target/quarkus-app/quarkus-run.jar.
If you want to build an über-jar, execute the following command:
./mvnw package -Dquarkus.package.jar.type=uber-jarThe application, packaged as an über-jar, is now runnable using java -jar target/*-runner.jar.
You can create a native executable using:
./mvnw package -DnativeOr, if you don't have GraalVM installed, you can run the native executable build in a container using:
./mvnw package -Dnative -Dquarkus.native.container-build=trueYou can then execute your native executable with: ./target/speaken-1.0.0-SNAPSHOT-runner
If you want to learn more about building native executables, please consult https://quarkus.io/guides/maven-tooling.
- LangChain4j Easy RAG (guide): Provides the Easy RAG functionality with LangChain4j
- LangChain4j pgvector embedding store (guide): Provides the pgvector Embedding store for Quarkus LangChain4j
- Hibernate ORM with Panache (guide): Simplify your persistence code for Hibernate ORM via the active record or the repository pattern
- RESTEasy Classic (guide): REST endpoint framework implementing Jakarta REST and more
- LangChain4j Ollama (guide): Provides the basic integration of Ollama with LangChain4j
Create your first JPA entity
Related Hibernate with Panache section...
This code is a very basic sample service to start developing with Quarkus LangChain4j using Easy RAG.
You have to add an extension that provides an embedding model. For that, you can choose from the plethora of extensions like quarkus-langchain4j-openai, quarkus-langchain4j-ollama, or import an in-process embedding model - these have the advantage of not having to send data over the wire.
In ./easy-rag-catalog/ you can find a set of example documents that will be used to create the RAG index which the bot (src/main/java/org/acme/Bot.java) will ingest.
On first run, the bot will create the RAG index and store it in easy-rag-catalog.json file and reuse it on subsequent runs.
This can be disabled by setting the quarkus.langchain4j.easy-rag.reuse-embeddings.enabled property to false.
Add it to a Rest endpoint:
@Inject
Bot bot;
@POST
@Produces(MediaType.TEXT_PLAIN)
public String chat(String q) {
return bot.chat(q);
}In a more complete example, you would have a web interface and use websockets that would provide more interactive experience, see ChatBot Easy RAG Sample for such an example.
Easily start your RESTful Web Services