RAGBOT CLI: a Python library for running and evaluating retrieval-augmented generation chatbots

Fine-tuning Retrieval-Augmented Generation (RAG) chatbots is challenging due to the many interdependent parameters affecting performance. RAGBOT CLI is a terminal-based Python tool built atop the LangChain framework that enables systematic experimentation with RAG configurations and automated evaluation using both quantitative metrics (BLEU, ROUGE-L, semantic similarity) and qualitative ones (contextual relevance, response relevance, factual fidelity). Unlike existing frameworks, RAGBOT CLI offers a modular, project-oriented architecture and supports hybrid evaluation strategies, making it suitable for academic and professional use. This paper describes the architecture, functionalities, and practical applications, showcasing its potential impact on the development and evaluation of RAG-based chatbots.

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Publication Details

Journal
SoftwareX
Published
2025-11-28
DOI
https://doi.org/10.1016/j.softx.2025.102458
Primary Topic
AI in Service Interactions
Type
article
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article

RAGBOT CLI: a Python library for running and evaluating retrieval-augmented generation chatbots

Cristina Tîrnȃucȃ, Marta Zorrilla, Rubén Martínez Amodia
SoftwareX
AI in Service Interactions
article

RAGBOT CLI: a Python library for running and evaluating retrieval-augmented generation chatbots

Cristina Tîrnȃucȃ, Marta Zorrilla, Rubén Martínez Amodia
article en

Abstract

Fine-tuning Retrieval-Augmented Generation (RAG) chatbots is challenging due to the many interdependent parameters affecting performance. RAGBOT CLI is a terminal-based Python tool built atop the LangChain framework that enables systematic experimentation with RAG configurations and automated evaluation using both quantitative metrics (BLEU, ROUGE-L, semantic similarity) and qualitative ones (contextual relevance, response relevance, factual fidelity). Unlike existing frameworks, RAGBOT CLI offers a modular, project-oriented architecture and supports hybrid evaluation strategies, making it suitable for academic and professional use. This paper describes the architecture, functionalities, and practical applications, showcasing its potential impact on the development and evaluation of RAG-based chatbots.

SoftwareXVol. 32
Universidad de Cantabria (ES)
Openalex Percentile: Top 80%
AI in Service Interactions
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