Aquiles-ai

I’ve been developing Aquiles-RAG for about a month. It’s a high-performance RAG server that uses Redis as the vector database and FastAPI for the API layer. The project’s goal is to provide a production-ready infrastructure you can quickly plug into your company or AI pipeline, while remaining agnostic to embedding models — you choose the embedding model and how Aquiles-RAG integrates into your workflow. What it offers - An abstraction layer for RAG designed to simplify integration into existing pipelines. - A production-grade environment (with an Open-Source version to reduce costs). - API compatibility between the Python implementation (FastAPI + Redis) and a JavaScript version (Fastify + Redis — not production ready yet), sharing payloads to maximize compatibility and ease adoption. Why I built it I believe every RAG tool should provide an abstraction and availability layer that makes implementation easy for teams and companies, letting any team obtain a production environment quickly without heavy complexity or large expenses.