The rapid evolution of large language models has shifted focus from general-purpose chatbots to highly specialized systems grounded in private data. Organizations and individual self-hosters want to query their own PDFs, markdown files, and database exports without exposing sensitive information to external APIs. This requirement has made Retrieval-Augmented Generation (RAG) the standard architecture for modern AI applications.

However, building a RAG pipeline from scratch is complex. It typically requires configuring document parsers, setting up a vector database, managing text chunking strategies, and building a user interface that ties everything together. While developer frameworks exist