From Manual RAG to Real Retrieval — Embedding-Based RAG with NVIDIA NIM
The article discusses the transition from a manual approach to a more efficient retrieval-augmented generation (RAG) method using NVIDIA NIM. It emphasizes the importance of embedding-based retrieval to improve the performance of AI applications by selecting relevant information from a larger knowledge base. The author provides a tutorial on implementing this system, highlighting the simplicity of using Python and NVIDIA's embedding model.
- ▪The manual approach to RAG becomes impractical when dealing with large knowledge bases.
- ▪Embedding-based retrieval allows for efficient selection of relevant information at query time.
- ▪NVIDIA's nv-embedqa-e5-v5 model is specifically designed for question-answer retrieval.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3943111) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Torkian Posted on May 23 From Manual RAG to Real Retrieval — Embedding-Based RAG with NVIDIA NIM #nvidia #ai #python #tutorial NVIDIA NIM from First Call to Working Agent (2 Part Series) 1 Build Your First AI App with NVIDIA NIM in 30 Minutes 2 From Manual RAG to Real Retrieval — Embedding-Based RAG with NVIDIA NIM In Part 1, we built a USC campus assistant by pasting a five-line knowledge base directly into the prompt. That works when "the data" fits in your head.
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