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# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "ragR" in publications use:'
type: software
license: GPL-3.0-only
title: 'ragR: Retrieval-Augmented Generation and RAG Evaluation Tools'
version: 0.1.0
abstract: '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" <https://doi.org/10.48550/arXiv.2005.11401>. The evaluation metrics are
  based on Es et al. (2024) "RAGAS: Automated Evaluation of Retrieval Augmented Generation"
  <https://doi.org/10.18653/v1/2024.eacl-demo.16>.'
authors:
- family-names: Rehman
  given-names: Muhammad Aimal
  email: rehman.aimal@gmail.com
- family-names: Lu
  given-names: Zhili
- family-names: Yeh
  given-names: Chi-Kuang
repository: https://cran.r-universe.dev
repository-code: https://github.com/aimalrehman92/ragR
commit: f01a408bd5d8935e6500b18833e581ed89998b6e
url: https://github.com/aimalrehman92/ragR
date-released: '2026-07-22'
contact:
- family-names: Rehman
  given-names: Muhammad Aimal
  email: rehman.aimal@gmail.com