Gul55555/ragbench-corpus

RAGBench Corpus

A small, focused document corpus designed for evaluating Retrieval-Augmented Generation (RAG) systems and comparing different retrieval and document chunking strategies.

Dataset Description

RAGBench Corpus contains 20 short documents covering concepts related to modern information retrieval and RAG systems.

The corpus is designed to be used together with the RAGBench Queries dataset to benchmark retrieval performance.

Topics covered include:

• —Dense retrieval
• —BM25
• —Reciprocal Rank Fusion (RRF)
• —Cross-encoder reranking
• —Semantic chunking
• —Fixed-size chunking
• —Parent-child chunking
• —Late chunking
• —Vector databases
• —Query expansion
• —HyDE
• —Query routing
• —Knowledge graphs
• —GraphRAG
• —Contextual embeddings
• —RAG evaluation

Dataset Structure

The dataset contains two fields:

The dataset contains 20 documents.

Example:

{
  "doc_id": "doc_01",
  "content": "Reciprocal rank fusion combines ranked lists from multiple retrievers..."
}