AGENTIC-SYSTEMSMarch 20, 20261 min read21 views

DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation

DynaRAG is a novel RAG framework that integrates external APIs to handle time-sensitive queries, significantly improving accuracy and reducing hallucinations in AI-driven question-answering systems.

Computer Science > Computation and Language

Title:DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation

View PDF HTML (experimental)Abstract:We present DynaRAG, a retrieval-augmented generation (RAG) framework designed to handle both static and time-sensitive information needs through dynamic knowledge integration. Unlike traditional RAG pipelines that rely solely on static corpora, DynaRAG selectively invokes external APIs when retrieved documents are insufficient for answering a query. The system employs an LLM-based reranker to assess document relevance, a sufficiency classifier to determine when fallback is necessary, and Gorilla v2 -- a state-of-the-art API calling model -- for accurate tool invocation. We further enhance robustness by incorporating schema filtering via FAISS to guide API selection. Evaluations on the CRAG benchmark demonstrate that DynaRAG significantly improves accuracy on dynamic questions, while also reducing hallucinations. Our results highlight the importance of dynamic-aware routing and selective tool use in building reliable, real-world question-answering systems.

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Source: arXiv cs.AI Recent

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