spRAG is a retrieval system that’s designed to handle complex real-world queries over dense text, like legal documents and financial reports. As far as we know, it produces the most accurate and reliable results of any RAG system for these kinds of tasks. For example, on FinanceBench, which is an especially challenging open-book financial question answering benchmark, spRAG gets 83% of questions correct, compared to 19% for the vanilla RAG baseline (which uses Chroma + OpenAI Ada embeddings + LangChain).

You can find more info about how it works and how to use it in the project’s README. We’re also very open to contributions. We especially need contributions around integrations (i.e. adding support for more vector DBs, embedding models, etc.) and around evaluation.