GraphRAG Benchmark Tool: Enhancing LLM Retrieval Capabilities with Graph Databases

graph-rag-benchmark is an end-to-end GraphRAG reasoning pipeline that enables multi-hop retrieval via the TigerGraph database, integrates with the Groq API for high-speed generation, and provides performance metric comparisons to help developers evaluate the advantages of graph-enhanced RAG over traditional baseline LLMs. The project uses a dual-pipeline architecture (baseline LLM and GraphRAG-enhanced), with an interactive frontend built using Streamlit that supports real-time metric display and fallback to a local knowledge base.

Traditional RAG is based on vector similarity search and struggles to capture structured relationships between entities when handling multi-hop reasoning; GraphRAG uses graph databases to store query knowledge and obtains structured context through multi-hop traversal. The graph-rag-benchmark project aims to demonstrate its advantages and provide comparisons with baseline LLMs.

The project uses a dual-pipeline design: 1. Baseline LLM pipeline (user query → direct generation via Groq API); 2. GraphRAG-enhanced pipeline (user query → keyword matching → TigerGraph multi-hop traversal → context acquisition → Groq generation). The tech stack includes TigerGraph (native distributed graph database supporting multi-hop traversal and real-time performance), Groq API (high-speed LLaMA3 inference), and Streamlit (interactive dark-themed dashboard).

The project's code structure is modular, including modules like config, main, data, graph (connection/schema/loader/query), inference, llm, eval, and dashboard. The built-in knowledge base covers 25 entities and over 30 relationships, spanning fields such as machine learning, NLP, graph technology, and mainstream frameworks, and is extensible and customizable.

The system provides real-time metric comparisons: token usage, response time, cost estimation, and context quality. To address the dormancy issue of TigerGraph cloud instances, a local knowledge base fallback mode is designed to ensure application availability.

Compared to traditional vector retrieval, GraphRAG has: 1. Structured knowledge representation (understanding hierarchical relationships and tracking multi-hop chains); 2. Enhanced interpretability (displaying retrieved entities and relationship paths); 3. Reduced hallucinations (anchoring to structured factual knowledge).

GraphRAG is suitable for scenarios such as enterprise knowledge management, scientific literature analysis, and medical diagnosis support. The project demonstrates the potential of graph-enhanced RAG, which is expected to become the next-generation standard paradigm for RAG, providing developers with a runnable prototype and technical reference.