Agentic systems, which leverage tools like knowledge graphs, often struggle with effectively retrieving relevant information from these graphs to inform their decision-making process, leading to inaccuracies and inefficiencies. This article addresses the need for robust evaluation of graph retrieval components within these systems. By Tomaz Bratanic.
The authors propose a new evaluation framework, Graph Retrieval Evaluation Pipeline (GREP), built on LangChain, designed to systematically assess graph retrieval performance across various query types and graph structures. GREP introduces automated tests focusing on relevance, completeness, and faithfulness of retrieved subgraph information.
Experiments utilizing GREP on a medicine-focused knowledge graph reveal that current retrieval methods exhibit weaknesses in handling complex queries requiring multi-hop reasoning and often provide incomplete or irrelevant information. The tests highlight the sensitivity of performance to the specific query formulation used.
This work provides a crucial tool for developers building agentic systems, helping them pinpoint weaknesses in their graph retrieval modules and improve the reliability and accuracy of their agents. GREP facilitates faster iteration and more targeted optimization of these systems, especially for knowledge-intensive tasks. Good read!
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