An AI Agent-Based System for Retrieving Compound Information in Traditional Chinese Medicine
Traditional Chinese medicine (TCM), as a vital component of traditional healthcare systems, relies heavily on its chemical constituents, which serve as a bridge between ancient therapeutic theories and modern biomedical science. Efficient access to compound-related information is crucial for promoti...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/7/543 |
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| Summary: | Traditional Chinese medicine (TCM), as a vital component of traditional healthcare systems, relies heavily on its chemical constituents, which serve as a bridge between ancient therapeutic theories and modern biomedical science. Efficient access to compound-related information is crucial for promoting the modernization and scientific understanding of TCM. However, existing approaches primarily rely on fragmented databases and literature-based retrieval methods, which suffer from low intelligence, poor data integration, and limited retrieval efficiency.This study presents a novel AI agent-based retrieval system tailored for compound information in TCM. The core innovation of the system lies in its hybrid retrieval-augmented generation framework, which seamlessly combines structured database queries with semantic vector retrieval. Furthermore, it integrates knowledge from three complementary sources—locally built knowledge bases, domain-specific APIs, and open web search—allowing for comprehensive coverage and adaptive handling of diverse natural language queries. Experiments conducted on a benchmark dataset of 150 compound-related queries demonstrate that the system achieves a peak accuracy of 96.67% across multiple mainstream LLMs. Ablation studies further reveal that removing either the hybrid RAG or multi-source knowledge module leads to a notable accuracy decline, while the full system outperforms typical RAG baselines by over 25%. These results confirm the effectiveness and robustness of the proposed architecture in TCM compound retrieval, and highlight the advantage of combining structured matching with dynamic knowledge access in specialized biomedical applications. |
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| ISSN: | 2078-2489 |