# java-chat-with-documents

**Repository Path**: yami/java-chat-with-documents

## Basic Information

- **Project Name**: java-chat-with-documents
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-19
- **Last Updated**: 2025-03-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Java AI chatbot that uses your documents as context 

This app demonstrates how you can create a custom AI chatbot that can use your own documents to answer questions using RAG (retrieval augmented generation).
The chatbot uses [LangChain4j](https://github.com/langchain4j/langchain4j) and the OpenAI API to generate responses and [Vaadin](http://vaadin.com/) to create the user interface.

> [!IMPORTANT]
> Before you can use the application you need to:
> 1. Configure the documentation location
> 2. Configure either OpenAi or a local LLM

## 🛠️ Configuration

### Configuring documentation location

Update the `docs.location` property in `application.properties` to point to a folder with relevant documents. 
LangChain4j uses Apache Tika internally when processing the files, so most file types work.

### Using Open AI

OpenAI gives you better quality answers but requires you to send data to a 3rd party.

To use OpenAI, get an [API key](https://platform.openai.com/api-keys) and configure it in `application.properties`. 
Optionally, you can also configure the model in the properties. 

### Using a local LLM

Using a local model allows you to keep your data on your local computer, but the quality of answers will not be as good as with OpenAI.

Install [Ollama](https://ollama.com/) and the `llama3` model.
Comment out the OpenAI section of `application.properties` and uncomment the Ollama section.

### Optional: Embedding store (Vector DB)

By default, the application uses an in-memory embedding store. This is fine for demos and small amounts of data. 
If you need to store more documents, consider using any of the [embedding stores that LangChain4j supports](https://docs.langchain4j.dev/integrations/embedding-stores/).

## ▶️ Running the application

The project is a standard Maven project. To run it from the command line,
type `mvnw` (Windows), or `./mvnw` (Mac & Linux), then open
http://localhost:8080 in your browser.

You can also import the project to your IDE of choice as you would with any
Maven project. Read more on [how to import Vaadin projects to different IDEs](https://vaadin.com/docs/latest/guide/step-by-step/importing) (Eclipse, IntelliJ IDEA, NetBeans, and VS Code).