Graph RAG vs Vector RAG: When to Use Each
The article discusses two approaches to Retrieval-Augmented Generation (RAG): Vector RAG and Graph RAG. Vector RAG focuses on similarity-based retrieval, while Graph RAG emphasizes relationship-based retrieval. The article outlines the strengths and weaknesses of each method, as well as when to use them based on specific query needs.
- ▪Vector RAG uses chunked documents and embeddings stored in a vector database for similarity-based retrieval.
- ▪Graph RAG models knowledge as nodes and relationships, allowing for retrieval of related subgraphs.
- ▪Choosing between Vector RAG and Graph RAG depends on the complexity of the queries and the need for relationship context.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3942968) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Recep Çiftçi Posted on May 22 • Originally published at recep-ciftci.prep-test.com Graph RAG vs Vector RAG: When to Use Each #ai #architecture #llm #rag Graph RAG vs Vector RAG: When to Use Each Retrieval-Augmented Generation (RAG) helps LLMs use external knowledge more reliably. In practice, two patterns show up often: Vector RAG and Graph RAG. Both try to solve the same problem: bring relevant context to the model. They just do it with different data models.
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