I Rewrote a Python RAG Library in Rust

A faster document indexing system for RAG pipelines. We benchmarked two implementations—one in Python (from VectifyAI), one in Rust—that let you index documents and retrieve relevant sections for LLM queries. How it works: Index your documents (builds a searchable structure) User asks a question System finds the most relevant section Pass that context to an LLM for the answer Why it matters: At production scale (1MB+ documents), Rust is 1.68x faster with 27x more stable latency. Python works fine for small documents but gets unpredictable at scale.