This article details the construction of a self-hosted Retrieval-Augmented Generation (RAG) chatbot designed to provide accurate answers grounded in a controlled knowledge base. The system employs a dual-agent pipeline for distinct reasoning steps: one agent selects tools and arguments for retrieval, and a second agent refines the retrieved information into a final, grounded answer. A key feature is the automatic failover mechanism, which seamlessly switches to a backup LLM if the primary model is unavailable or rate-limited, ensuring query continuity. AI
IMPACT Provides a technical blueprint for building more robust and reliable RAG chatbots with automatic LLM failover.
RANK_REASON The article describes the technical implementation of a chatbot system, not a new product release or frontier research.
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