When LLMs are applied in the veterinary field, they often produce serious hallucinations and logical restrictions, especially in the accurate diagnosis of bovine disease, where accuracy is crucial. To meet this challenge, this paper proposes GraphRAG-Vet, a Knowledge Graph Retrieval-Augmented Generation framework specifically designed for the dairy industry. First, we constructed a domain knowledge map comprising 2500 elements and 3000 relationships, covering high-frequency diseases in cows such as mastitis and ketosis. Second, the semantic-to-password parsing module is designed to retrieve disease symptom subgraphs from the Neo4j database accurately. Finally, the hard constraint injection mechanism is introduced to force LLMs to generate diagnoses strictly in accordance with the retrieved graph context, thereby implementing the “refuse to answer” function for foreign queries. The experimental results showed that GraphRAG-Vet achieved 100% accuracy in diagnosing core infectious diseases and had an almost-zero hallucination rate compared with baseline LLMs. This study provides a reliable, low-resource solution for automated veterinary consultation.
Qu et al. (2026) studied this question.