Dabarqus gives you a practical way to add retrieval-augmented generation (RAG) to your app in less than 9 lines of code. Chat with your PDFs, summarize emails and messaging, and digest a vast range of facts, figures, and reports. A dash of genius for your LLM.
Hey everyone,
If you're a developer, building a basic RAG solution is pretty straightforward. There are tons of tutorials and how-tos, as well as Python code to reuse. But, if you're deploying your RAG solution within a company, or on end-user PCs, you will also have to figure out some potentially tricky deployment and maintenance issues. That also means deploying Python, a vector database, the right embedding AI model, and possibly dealing with licensing challenges.
Dabarqus was created to address these issues with a stand-alone, all-in-one solution with no runtime dependencies. It's written in C++ and has built-in vector search, an industry-standard embedding model, and a REST API for easy development integration.
I made an example python chatbot that uses Dabarqus with Ollama, and put it in the Github repo.
I'd love your feedback – is anything missing? What would make Dabarqus more useful?
Thanks for checking this out. Looking forward to your thoughts.
e/ectric Curtis