RAG for Multi-Tool Integration and Smart Workflows

Abstract

Large Language Models (LLMs) have demonstrated impressive performance in natural language understanding and generation tasks. However, standalone LLMs are susceptible to hallucinations and are unable to retrieve real-time or domain-specific knowledge. Retrieval-Augmented Generation (RAG) addresses these limitations by grounding generated responses in relevant external knowledge sources. This paper proposes an intelligent RAG-based system that incorporates multi-tool integration and smart workflow orchestration. The proposed system dynamically retrieves relevant documents from vector databases and invokes external tools, such as calculators and application programming interfaces (APIs), to generate accurate, reliable, and context-specific responses. The system will be implemented using embedding models, vector databases, LLMs, and tool-invocation mechanisms.