Online-Optimized RAG for Tool Use and Function Calling

• URL: http://arxiv.org/abs/2509.20415v2
• Date: Fri, 26 Sep 2025 03:20:04 GMT
• Title: Online-Optimized RAG for Tool Use and Function Calling
• Authors: Yu Pan, Xiaocheng Li, Hanzhao Wang,
• Abstract summary: retrieval-augmented generation (RAG) drives tool use and function calling by embedding user queries to pre-specified tool/function descriptions.<n>Online-d RAG adapts retrieval embeddings from live interactions using minimal feedback.
• Score: 10.294181998196555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In many applications, retrieval-augmented generation (RAG) drives tool use and function calling by embedding the (user) queries and matching them to pre-specified tool/function descriptions. In this paper, we address an embedding misalignment issue that often arises in practical applications due to imperfect embedding models or noisy descriptions; such misalignment may lead to incorrect retrieval and task failure. We introduce Online-Optimized RAG, a deployment-time framework that continually adapts retrieval embeddings from live interactions using minimal feedback (e.g., task success). Online-Optimized RAG applies lightweight online gradient updates with negligible per-query latency and requires no changes to the underlying LLM. The method is plug-and-play: it supports both single- and multi-hop tool use, dynamic tool inventories, and $K$-retrieval with re-ranking. We provide a problem-dependent theoretical analysis that quantifies how the method's performance depends on the initialization quality of the embeddings and other related quantities. Across diverse tool-use and document-retrieval scenarios, our Online-Optimized RAG consistently improves tool selection accuracy and end-task success, thus providing a simple, practical path to robust, self-improving RAG systems.

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