quarkus-langchain-doc-helper

This project uses Quarkus, the Supersonic Subatomic Java Framework, integrated with LangChain4j library using Retrieval-Augmented Generation process and providing a REST API to interact with Large Language Models.

• Large Language Models (LLMs) are AI-based systems designed to understand, generate, and manipulate human language, showcasing advanced natural language processing capabilities.
• LangChain4j library offers a declarative approach to interact with diverse LLMs like OpenAI, Hugging Face, Ollama, or Jlama.
• Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. RAG extends the already powerful capabilities of LLMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model.

Prerequisites

• Java 21
• A running container environment, such as Docker or Podman

Running the application in dev mode

You can run your application in dev mode that enables live coding using:

mvn quarkus:dev

NOTE: Quarkus now ships with a Dev UI, which is available in dev mode only at http://localhost:8080/q/dev/.

Requests

You can interact with the LLM using the following two ways:

1. Make a question:

curl --request POST \
  --url http://localhost:8080/doc/help/question \
  --header 'Content-Type: application/json' \
  --data '{
	"question": "Is Runtime Verification useful in Internet of Things ecosystems?",
	"style": "informal",
	"size": "short"
}'

2. Require a summary about a keyphrase:

curl --request POST \
  --url http://localhost:8080/doc/help/summary \
  --header 'Content-Type: application/json' \
  --data '{
	"keyPhrase": "Runtime Verification",
	"style": "academic"
}'

Related Documentation

• LangChain4j OpenAI (guide): Provides the basic integration with LangChain4j
• RAG process benefits
• Edge-Based Runtime Verification for the Internet of Things paper