  The retrieval-augmented generation (RAG) enables retrieval of relevant
information from an external knowledge source and allows large language models
(LLMs) to answer queries over previously unseen document collections. However,
it was demonstrated that traditional RAG applications perform poorly in
answering multi-hop questions, which require retrieving and reasoning over
multiple elements of supporting evidence. We introduce a new method called
Multi-Meta-RAG, which uses database filtering with LLM-extracted metadata to
improve the RAG selection of the relevant documents from various sources,
relevant to the question. While database filtering is specific to a set of
questions from a particular domain and format, we found out that Multi-Meta-RAG
greatly improves the results on the MultiHop-RAG benchmark. The code is
available at https://github.com/mxpoliakov/Multi-Meta-RAG.
