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ragR  

Retrieval-Augmented Generation and RAG Evaluation Tools
View on CRAN: Click here


Download and install ragR package within the R console
Install from CRAN:
install.packages("ragR")

Install from Github:
library("remotes")
install_github("cran/ragR")

Install by package version:
library("remotes")
install_version("ragR", "0.1.0")



Attach the package and use:
library("ragR")
Maintained by
Muhammad Aimal Rehman
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-07-22
Latest Update: 2026-07-22
Description:
Provides tools for document ingestion, embedding storage, retrieval-augmented generation (RAG), and evaluation of question-answering systems. The package includes an R-native vector store, wrappers for OpenAI embedding and chat-completion application programming interfaces (APIs), question-answering logging utilities, and large language model (LLM)-based evaluation metrics for context precision, context recall, answer relevance, and faithfulness. These metrics are based on the Retrieval-Augmented Generation Assessment (RAGAS) framework. The retrieval-augmented generation methodology is described by Lewis et al. (2020) "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" <doi:10.48550/arXiv.2005.11401>. The evaluation metrics are based on Es et al. (2024) "RAGAS: Automated Evaluation of Retrieval Augmented Generation" <doi:10.18653/v1/2024.eacl-demo.16>.
How to cite:
Muhammad Aimal Rehman (2026). ragR: Retrieval-Augmented Generation and RAG Evaluation Tools. R package version 0.1.0, https://cran.r-project.org/web/packages/ragR. Accessed 02 Oct. 2026.
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