# BKAi: A University Admissions Consulting AI System Based on Multi-Agent RAG Architecture

> BKAi is an AI admissions consulting system designed specifically for Ho Chi Minh City University of Technology. It uses a Multi-Agent RAG architecture to address the hallucination problem of traditional large language models and integrates a semantic caching system to improve response efficiency.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-20T03:41:27.000Z
- 最近活动: 2026-05-20T03:48:17.924Z
- 热度: 150.9
- 关键词: Multi-Agent RAG, Agentic RAG, 招生咨询, AI系统, 语义缓存, 大语言模型, 幻觉问题, 教育科技
- 页面链接: https://www.zingnex.cn/en/forum/thread/bkai-ragai
- Canonical: https://www.zingnex.cn/forum/thread/bkai-ragai
- Markdown 来源: floors_fallback

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## BKAi: Guide to the Multi-Agent RAG-Based University Admissions Consulting AI System

BKAi is an AI admissions consulting system designed specifically for Ho Chi Minh City University of Technology. It adopts a Multi-Agent RAG architecture to solve the hallucination problem of traditional large language models and combines semantic caching to improve response efficiency. The system aims to replace manual consulting, provide 7x24 accurate and instant admissions information services, and offer a reliable practical reference for AI applications in educational institutions.

## Project Background and Problem Definition

With the development of generative AI, educational institutions are exploring the use of LLMs to assist in admissions consulting. However, traditional RAG systems have the problem of hallucinations (fabricating incorrect information). Ho Chi Minh City University of Technology faces a large number of consulting demands; manual customer service is costly and cannot respond 24 hours a day. Therefore, there is an urgent need for an accurate and continuously online AI consulting system.

## Core Components of the BKAi System Architecture

BKAi uses a Multi-Agent RAG architecture, including four core components: 1. Retrieval Agent: Semantic search to accurately obtain knowledge base information; 2. Verification Agent: Review results and answers to ensure accuracy and avoid hallucinations; 3. Generation Agent: Generate fluent answers based on verified information; 4. Semantic Caching System: Identify similar queries and reuse results to improve efficiency.

## Technical Implementation Details

Multi-agent collaboration process: Query → Retrieval Agent recalls documents → Verification Agent verifies → Generation Agent constructs answers → Re-verify → Cache record. Semantic caching principle: Query vectorization → Cosine similarity calculation → Threshold determination for cache hit, supporting reuse of semantically equivalent queries (e.g., "Computer Science admission requirements" and "CS entry conditions").

## Practical Application Value

Innovation for admissions work: Second-level response eliminates queuing, unified knowledge base ensures information consistency, 24/7 all-day service, and easy expansion by updating the knowledge base. Insights for AI development: Multi-agent verification can control hallucinations, semantic caching improves efficiency in high-frequency scenarios, and deep integration of domain knowledge is the key to high-quality services.

## Future Development Directions

Future expansion directions for BKAi: Multi-language support (English, Chinese, etc.), personalized major recommendations, integration with student information systems/application platforms, and continuous learning optimization strategies.

## Conclusion and Insights

BKAi is an excellent case of combining cutting-edge AI with educational needs, proving that reasonable architecture design allows LLMs to be safely deployed in high-reliability scenarios. Multi-agent verification and semantic caching are core innovations, providing engineering practice references for AI applications in other fields. Technology selection is the foundation; architecture design and reliability engineering determine the success or failure of AI applications.