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OnboardEase - Dynamic Learning Assistant with Langchain4j & Spring AI 🦜️🔗 + 🍃🤖️

Logo

OnboardEase: A dynamic learning assistant designed to simplify the onboarding and training process for new hires. Users can upload documents or enter URLs for training materials, and the application provides a platform for asking training-related queries. Built with Spring Boot, Langchain4j and Spring AI.

💡 This application is not business oriented and my focus is mostly on technical part, I just want to play with LLM/RAG chatbot assistant by building a sample app from scratch with two main libraries in java ecosystem : Spring AI 🍃 and Langchain4j 🦜️.

🌀 This Application is in-progress and I will add new features over time. 🌀

Application Screenshot

OnboardEase

Features

  • ✅ Choose between two libraries for chatbot assistant : Langchain4j or Spring AI
  • ✅️ Upload documents or enter URLs for training materials
  • ✅️ Ingest documents and give context to the chatbot
  • ✅️ Ask training-related queries

Tech Stack

Setup & Installation

  • 1️⃣ Clone the project on your local machine

    git clone https://github.com/miliariadnane/onboardease-rag-training-assistant.git

  • 2️⃣ Install dependencies

    mvn clean install

  • 3️⃣ Run docker-compose file :

    docker-compose up -d

  • 4️⃣ Open PgAdmin UI and create two databases : vector_store and langchain_vector_store
    • 4️⃣- 1️⃣ For vector_store database, create an extension (do this once in each database where you want to use it) : 
      CREATE EXTENSION vector;
  • 5️⃣ Configure your openai API key in application.properties file or in your environment variables
  • 6️⃣ Run the application

    mvn spring-boot:run

  • 🚀️ Open your browser and navigate to http://localhost:8080 (Enjoy the app 🎉️)

How RAG Works under the hood ?

RAG

  • 1️⃣ Ingest domain data and give context to the chatbot.
  • 2️⃣ Reader to retrieve text from the documents.
  • 3️⃣ Splitter to split the text into chunks.
  • 4️⃣ Embedding to convert the text into vectors.
  • 5️⃣ Store the vectors in the vector store database.
  • 6️⃣ User ask the question to the chatbot.
  • 7️⃣ Splitter to split the question into chunks.
  • 8️⃣ Embedding to convert the question into vectors.
  • 9️⃣ Similarity search to find the most relevant chunks between the question and the documents ingested.
  • 🔁️ Loop through the chunks and return the most relevant answer to the user.
  • 🎉️ Return the answer to the user.

Diagram of the Application

OnboardEase

Roadmap (In Progress)

  • 🚧 Fix AI text embedding issue (Spring AI)
  • 🚧 Implement similarity search to find the most relevant answer to the user (Spring AI)
  • 🚧 Add system message prompt (Spring AI)

Support

  • ⭐️️ Star this repository if you find it useful.
  • 🐛️ If you find a bug, raise an issue or fix it and send a pull request.
  • 📢️ If you have any feature requests, raise an issue or send a pull request.
  • 🤲 If you have a moment, don't forget to make a duaa for me and my parents.