I’ve been working on a library for RAG systems called quorumrag. The core issue I wanted to solve was "retriever confidence." In most RAG pipelines, you have one retriever, and if it grabs a bad chunk, the LLM hallucinates. I wanted to see if I could improve recall by running multiple retrievers in parallel (using tokio) and only passing evidence to the LLM if they actually agree (the quorum). Technical details: Parallelism: Uses tokio for concurrent execution. Storage: Uses tantivy for BM25 and I'm handling semantic search indices in-process. Logic: It uses Reciprocal Rank Fusion (RRF) for ranking. Goal: The project is a "single binary" solution. No Python runtime required. It’s definitely a work in progress. I’m currently at 19/20 recall on a small internal test set compared to 14/20 with a single-retriever baseline, but I know there’s a lot of room for improvement. I’m looking for feedback on my trait architecture for the retrievers and how I’m handling the concurrency.…