KIMAs: A Configurable Knowledge Integrated Multi-Agent System
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917921461108736 |
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| author | Li, Zitao Wei, Fei Xie, Yuexiang Gao, Dawei Kuang, Weirui Ma, Zhijian Qian, Bingchen Li, Yaliang Ding, Bolin |
| author_facet | Li, Zitao Wei, Fei Xie, Yuexiang Gao, Dawei Kuang, Weirui Ma, Zhijian Qian, Bingchen Li, Yaliang Ding, Bolin |
| contents | Knowledge-intensive conversations supported by large language models (LLMs) have become one of the most popular and helpful applications that can assist people in different aspects. Many current knowledge-intensive applications are centered on retrieval-augmented generation (RAG) techniques. While many open-source RAG frameworks facilitate the development of RAG-based applications, they often fall short in handling practical scenarios complicated by heterogeneous data in topics and formats, conversational context management, and the requirement of low-latency response times. This technical report presents a configurable knowledge integrated multi-agent system, KIMAs, to address these challenges. KIMAs features a flexible and configurable system for integrating diverse knowledge sources with 1) context management and query rewrite mechanisms to improve retrieval accuracy and multi-turn conversational coherency, 2) efficient knowledge routing and retrieval, 3) simple but effective filter and reference generation mechanisms, and 4) optimized parallelizable multi-agent pipeline execution. Our work provides a scalable framework for advancing the deployment of LLMs in real-world settings. To show how KIMAs can help developers build knowledge-intensive applications with different scales and emphases, we demonstrate how we configure the system to three applications already running in practice with reliable performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09596 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KIMAs: A Configurable Knowledge Integrated Multi-Agent System Li, Zitao Wei, Fei Xie, Yuexiang Gao, Dawei Kuang, Weirui Ma, Zhijian Qian, Bingchen Li, Yaliang Ding, Bolin Artificial Intelligence Multiagent Systems Knowledge-intensive conversations supported by large language models (LLMs) have become one of the most popular and helpful applications that can assist people in different aspects. Many current knowledge-intensive applications are centered on retrieval-augmented generation (RAG) techniques. While many open-source RAG frameworks facilitate the development of RAG-based applications, they often fall short in handling practical scenarios complicated by heterogeneous data in topics and formats, conversational context management, and the requirement of low-latency response times. This technical report presents a configurable knowledge integrated multi-agent system, KIMAs, to address these challenges. KIMAs features a flexible and configurable system for integrating diverse knowledge sources with 1) context management and query rewrite mechanisms to improve retrieval accuracy and multi-turn conversational coherency, 2) efficient knowledge routing and retrieval, 3) simple but effective filter and reference generation mechanisms, and 4) optimized parallelizable multi-agent pipeline execution. Our work provides a scalable framework for advancing the deployment of LLMs in real-world settings. To show how KIMAs can help developers build knowledge-intensive applications with different scales and emphases, we demonstrate how we configure the system to three applications already running in practice with reliable performance. |
| title | KIMAs: A Configurable Knowledge Integrated Multi-Agent System |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2502.09596 |