Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/marcushellberg/java-chat-with-documents
Chat with an AI that's aware of your documents.
https://github.com/marcushellberg/java-chat-with-documents
ai langchain4j spring-boot vaadin
Last synced: about 9 hours ago
JSON representation
Chat with an AI that's aware of your documents.
- Host: GitHub
- URL: https://github.com/marcushellberg/java-chat-with-documents
- Owner: marcushellberg
- License: mit
- Created: 2024-05-07T00:48:17.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-05-16T13:09:52.000Z (6 months ago)
- Last Synced: 2024-11-07T11:44:07.778Z (8 days ago)
- Topics: ai, langchain4j, spring-boot, vaadin
- Language: Java
- Homepage:
- Size: 162 KB
- Stars: 64
- Watchers: 4
- Forks: 14
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
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).