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Related Experiment Video

Updated: Apr 12, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Enhancing the capabilities of large language models for API calls through knowledge graphs.

Ye Yang¹, Xue Xiao², Ping Yin²

¹INSPUR GROUP CO., LTD, No. 1036, Langchao Road, High-tech Zone, Jinan City, 250101, Shandong Province, China. yeyang20@fudan.edu.cn.

Scientific Reports

|April 10, 2026

Summary

KG2data enhances meteorological data analysis by integrating knowledge graphs and large language models (LLMs) for accurate API calls. This system overcomes LLM limitations, improving query handling and data retrieval in specialized domains.

Keywords:

API calls Knowledge Graphs Large Language Models Meteorology

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Last Updated: Apr 12, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Artificial Intelligence
Data Science
Meteorology

Background:

Large language models (LLMs) show promise for API calls in data analysis.
LLM tool usage is underexplored in knowledge-intensive fields like meteorology.
Existing LLMs struggle with domain-specific knowledge, complex queries, and specialized terminology.

Purpose of the Study:

To propose KG2data, a system integrating knowledge graphs, LLMs, and React agents for intelligent API calls in meteorology.
To enhance data acquisition and query handling in the meteorological domain.
To overcome limitations of LLMs in processing specialized terminology and complex queries.

Main Methods:

Developed KG2data system combining knowledge graphs, LLMs, React agents, and tool usage.
Tested API call accuracy using a virtual API.
Compared KG2data against baseline systems: chat2data (without knowledge) and RAG2data (with vector database).

Main Results:

KG2data achieved superior performance with a 1.43% name recognition failure rate, 0% hallucination recognition failure rate, and 88.57% API call accuracy.
KG2data significantly outperformed RAG2data (16%, 10%, 72.14%) and chat2data (7.14%, 8.57%, 71.43%) across key metrics.
The system demonstrated improved content retrieval, complex query handling, logical reasoning, and data integration.

Conclusions:

KG2data effectively addresses the challenge of limited domain-specific knowledge in LLMs for API calls.
Utilizing knowledge graphs as long-term memory enhances KG2data's ability to handle complex, specialized meteorological queries.
The KG2data system offers a novel approach for knowledge-based question answering and data analysis in knowledge-intensive industries, with lower computational costs.