I'd replace the "challenging real-world tasks" in the title with "dense text, like financial reports and legal documents". It sounds less general but that's a good thing.
The repo is only two weeks old, and looks it, so how do you think spRAG distinguishes itself? This is a crowded space with more established players.
The "vanilla RAG" benchmark figure you cite is not convincing because it can not be verified. Please share your benchmarking code.
That's great feedback. I actually went back and forth between those two descriptions. I agree that "dense text, like financial reports and legal documents" is more precise. Those are the kinds of use cases this project is built for.
I want to keep this project tightly scoped to just retrieval over dense unstructured text, rather than trying to build a fully-featured RAG framework.
FWIW nobody has created a great JavaScript framework experience yet. Closest we have for RAG is LlamaIndexTS or Langchainjs but both are full of bugs and have little LLM support. Their whole approach to supporting LLMs is writing bespoke wrappers for each.
Maybe it's the same on the Python side, but it feels like nobody has nailed the perfect LLM wrapper library yet. I would focus on dev experience - make it dead simple to load in files and use it from 0-1.
The repo is only two weeks old, and looks it, so how do you think spRAG distinguishes itself? This is a crowded space with more established players.
The "vanilla RAG" benchmark figure you cite is not convincing because it can not be verified. Please share your benchmarking code.