RAG Datamodel¶

PyTerrier-RAG uses an extended datamodel, building on the standard PyTerrier datamodel (Q, D, R), adapted for answer generation.

Type	Required Columns	Description
Type	Required Columns	Description
A	`["qid", "qanswer"]`	Generated answers
GA	`["qid", "gold_answer"]`	Gold truth answers (an array)

Different transformer classes make different tranasformations between these datatypes:

Retriever (Q \(\rightarrow\) R) – retrieves documents in response to a query. Example: pt.terrier.Retriever().

Reranker: (R \(\rightarrow\) R) – reranks retriever documents for a given query. Example: pyterrier_t5.MonoT5().

0-shot answer generation (Q \(\rightarrow\) A) – generates an answer without reference to any retrieved documents.

Reader: R \(\rightarrow\) A – generates an answer given retrieved documents, ala RAG. Example pyterrier_rag.readers.T5FiD().