KIMAs: A Configurable Knowledge Integrated Multi-Agent System

Large language models (LLMs) have had a profound impact on various aspects of people's lives, particularly as the foundational technology behind conversational applications such as chatbots. These models have become indispensable as virtual assistants, offering powerful capabilities for various tasks, including addressing common-sense queries, generating summaries for academic papers [16], and solving programming challenges and tasks [11]. Despite their impressive functionality, LLMs are of some limitations. Issues such as hallucinations and the inability to provide the most up-to-date information or private knowledge hinder their reliability in directly serving for knowledge-intensive applications. These shortcomings can be mitigated by integrating LLMs with external information in the input context [20, 28]. One notable approach is retrieval-augmented generation (RAG) techniques [1, 10], which enhances LLMs by equipping them with retrieval capabilities, allows LLMs to address questions that exceed the scope of their pre-trained internal knowledge. RAG has proven highly effective in improving performance on question-answering (QA) tasks emphasizing faithfulness to truths, showcasing its potential to bridge the gap between static pre-trained knowledge and dynamic, context-specific information. While many real-world applications have adopted RAG techniques [13, 22], open-source frameworks have also emerged to facilitate the adaptation of RAG to a wide range of tasks [14, 18] for the public to hold RAG application services themselves with local data. While these open-source RAG frameworks provide convenient starting points for building RAG-based applications, there remain significant opportunities for improvement, especially in more practical and complicated scenarios, e.g., efficient multi-source knowledge retrieval, which provides primary motivations for this paper.