"Spartans-GraphRAG: Token-Efficient Threat Intelligence with TigerGraph"
The Spartans-GraphRAG project demonstrates a token-efficient approach to threat intelligence using TigerGraph. By utilizing a knowledge graph, the project significantly reduces token consumption while maintaining analytical accuracy. The results indicate that GraphRAG outperforms traditional methods in efficiency and cost-effectiveness.
- ▪Spartans-GraphRAG was developed for the TigerGraph GraphRAG Inference Hackathon to improve LLM inference efficiency.
- ▪The project includes three pipelines: LLM-Only, Basic RAG, and GraphRAG, all using the same LLM for fair comparison.
- ▪The knowledge graph approach allows for better handling of complex relationships in cybersecurity data, leading to reduced token usage.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3935289) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Indra Posted on May 16 "Spartans-GraphRAG: Token-Efficient Threat Intelligence with TigerGraph" #cybersecurity #devchallenge #llm #rag Large Language Models are revolutionizing how we interact with data, but as they spread across industries, token consumption is exploding. Context windows are growing, but so are the bills. Basic Retrieval-Augmented Generation (RAG) often addresses this by stuffing massive chunks of text into the LLM's prompt based on vector similarity.
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