https://github.com/edeandrea/quarkus-langchain4j-rag-demo

https://github.com/edeandrea/quarkus-langchain4j-rag-demo

Last synced: about 2 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/edeandrea/quarkus-langchain4j-rag-demo
• Owner: edeandrea
• Created: 2024-11-11T18:43:29.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-11T18:45:21.000Z (2 months ago)
• Last Synced: 2024-11-11T19:37:50.951Z (2 months ago)
• Language: Java
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
A RAG example that creates a complex `RetrievalAugmentor`, combining data from an embedding store as well as a web search of the quarkus.io site.

See [`application.properties`](src/main/resources/application.properties) and [`RagRetriever`](src/main/java/dev/langchain4j/quarkus/deepdive/RagRetriever.java).

There are some things commented out in `RagRetriever` that can be uncommented to change how things work:

- `.queryTransformer(new CompressingQueryTransformer(chatLanguageModel))`

- In the `getQueryRouter` method you can switch the `QueryRouter` to use a `LanguageModelQueryRouter`

- In the `getContentAggregator` method you can switch between a few options for defining a `ContentAggregator`

You will need the following environment variables to run, depending on which features you are using:

- `OPENAI_API_KEY` to make calls to OpenAI

- `TAVILY_API_KEY` to call the `WebSearchEngine`

- `COHERE_API_KEY` (optional) if you want to do re-ranking