KIMAs：一种可配置的知识集成多智能体系统
KIMAs: A Configurable Knowledge Integrated Multi-Agent System
AI总结：
针对现有RAG框架在异构数据处理、对话上下文管理及低延迟响应等方面的不足，提出可配置知识集成多智能体系统KIMAs，通过四大核心机制提升性能，为LLMs实际部署提供可扩展框架并展示了三个实用配置案例。
摘要：
大型语言模型（LLMs）支持的知识密集型对话已成为最受欢迎且实用的应用之一，能在多方面协助人们。当前许多知识密集型应用以检索增强生成（RAG）技术为核心。尽管众多开源RAG框架推动了基于RAG的应用开发，但在处理主题和格式异构数据、对话上下文管理以及低延迟响应需求导致的复杂实际场景时，它们往往存在不足。本技术报告提出一种可配置的知识集成多智能体系统KIMAs以应对这些挑战。KIMAs具备灵活可配置的系统，用于集成多样化知识源，其特性包括：1）上下文管理与查询重写机制，以提升检索准确性和多轮对话连贯性；2）高效的知识路由与检索；3）简单但有效的过滤与参考生成机制；4）优化的可并行多智能体流水线执行。我们的工作为LLMs在实际场景中的部署推进提供了可扩展框架。为展示KIMAs如何帮助开发者构建不同规模和侧重点的知识密集型应用，我们演示了如何将系统配置为三个已实际运行且性能可靠的应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
