VetBot: An AI-Driven Veterinary Chatbot for Canine and Feline Care Guidance

Abstract

Pet owners often struggle to interpret symptoms, apply basic first aid, or judge when a veterinary visit is necessary. In provincial settings such as La Union, Philippines, limited clinic access and geographic distance compound these gaps. This study designed, developed, and evaluated VetBot: An AI-Driven Veterinary Chatbot for Canine and Feline Care Guidance, based on a Retrieval-Augmented Generation (RAG) architecture. The system was implemented with a Flutter mobile interface, a Django backend, PostgreSQL for transactional data, and ChromaDB for semantic retrieval, with Gemini 3 Flash as the language model. VetBot performs triage assessment, delivers general health guidance and safety-oriented first aid instructions, and provides limited home-based care advice for lowrisk cases. Supporting modules include pet profiling with a digital vet card, a clinic locator covering La Union, and accessibility features comprising speech-to-text, text-to-speech, and bilingual English and Tagalog support. The RAG pipeline was evaluated with the RAGAS framework on 99 clinical test queries. Faithfulness scored 0.889 and Answer Relevancy 0.906, indicating that responses drew on retrieved veterinary sources and addressed user queries directly. Context Precision (0.753) and Context Recall (0.691) reflected moderately high retrieval performance, with room for improvement in retrieval coverage and configuration. Answer Correctness scored 0.517; this lower score does not mean VetBot gave wrong answers, but rather that the metric compared responses word-for-word against a single pre-written reference answer, so any response that conveyed the same information in different wording was scored lower even if it was factually correct. Nine (9) licensed veterinarians evaluated VetBot's outputs on a 5-point Likert scale across nine criteria, yielding an overall mean of 4.56. Triage Report Utility received the highest rating (4.67); non-urgent scenarios received the highest urgency-level mean (4.65), followed by urgent (4.57) and emergency (4.47), with all three urgency levels remaining within the Strongly Agree range. Thirty-two (32) pet owners completed the System Usability Scale (SUS), producing a score of 79.61 (Grade A-, “Excellent”). The findings indicate that VetBot delivers reliable, clinically consistent pet health guidance and connects owners to professional veterinary services without replacing clinical judgment.