  Federated search systems aggregate results from multiple search engines,
selecting appropriate sources to enhance result quality and align with user
intent. With the increasing uptake of Retrieval-Augmented Generation (RAG)
pipelines, federated search can play a pivotal role in sourcing relevant
information across heterogeneous data sources to generate informed responses.
However, existing datasets, such as those developed in the past TREC FedWeb
tracks, predate the RAG paradigm shift and lack representation of modern
information retrieval challenges. To bridge this gap, we present FeB4RAG, a
novel dataset specifically designed for federated search within RAG frameworks.
This dataset, derived from 16 sub-collections of the widely used \beir
benchmarking collection, includes 790 information requests (akin to
conversational queries) tailored for chatbot applications, along with top
results returned by each resource and associated LLM-derived relevance
judgements. Additionally, to support the need for this collection, we
demonstrate the impact on response generation of a high quality federated
search system for RAG compared to a naive approach to federated search. We do
so by comparing answers generated through the RAG pipeline through a
qualitative side-by-side comparison. Our collection fosters and supports the
development and evaluation of new federated search methods, especially in the
context of RAG pipelines.
