Graph RAG-Tool Fusion
Elias Lumer, Pradeep Honaganahalli Basavaraju, Myles Mason, James A., Burke, Vamse Kumar Subbiah

TL;DR
This paper introduces Graph RAG-Tool Fusion, a method that combines vector retrieval with graph traversal to better capture tool dependencies, improving tool selection accuracy for large tool sets in LLM agents.
Contribution
It proposes a novel plug-and-play approach that integrates graph traversal with vector-based retrieval to capture nested tool dependencies, enhancing retrieval accuracy.
Findings
Achieves 71.7% and 22.1% improvements over naive RAG on ToolLinkOS and ToolSandbox.
Introduces ToolLinkOS, a benchmark with 573 fictional tools and multiple dependencies.
Demonstrates significant performance gains in tool retrieval accuracy.
Abstract
Recent developments in retrieval-augmented generation (RAG) for selecting relevant tools from a tool knowledge base enable LLM agents to scale their complex tool calling capabilities to hundreds or thousands of external tools, APIs, or agents-as-tools. However, traditional RAG-based tool retrieval fails to capture structured dependencies between tools, limiting the retrieval accuracy of a retrieved tool's dependencies. For example, among a vector database of tools, a "get stock price" API requires a "stock ticker" parameter from a "get stock ticker" API, and both depend on OS-level internet connectivity tools. In this paper, we address this limitation by introducing Graph RAG-Tool Fusion, a novel plug-and-play approach that combines the strengths of vector-based retrieval with efficient graph traversal to capture all relevant tools (nodes) along with any nested dependencies (edges)…
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Taxonomy
TopicsGraph Theory and Algorithms
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Attention Is All You Need · Linear Warmup With Linear Decay · Weight Decay · WordPiece · Attention Dropout · Byte Pair Encoding · Layer Normalization · Residual Connection · Dense Connections
