PyTerrier-RAG is an extension for PyTerrier that makes it easier to produce retrieval augmented generation pipelines. PyTerrier-RAG supports:
- Easy access to common QA datasets
- Pre-built indices for common corpora
- Popular reader models, such as Fusion-in-Decoder, LLama
- Evaluation measures
As well as access to all of the retrievers (sparse, learned sparse and dense) and rerankers (from MonoT5 to RankGPT) accessible through the wider PyTerrier ecosystem.
Try it out here on Google Colab now by cliking the "Open in Colab" button!
- Sparse Retrieval with FiD and FlanT5 readers: sparse_retrieval_FiD_FlanT5.ipynb
- Craig Macdonald, University of Glasgow
- Jinyuan Fang, University of Glasgow
- Andrew Parry, University of Glasgow
- Zaiqiao Meng, University of Glasgow
- Sean MacAvaney, University of Glasgow
