Tutorial: Semantic Search, RAG and Index Vector Databases

Tutorial: Semantic Search, RAG and Index Vector Databases

Watch the YouTube video, here.

It is becoming difficult to keep track of all the technologies and associated terminology, with the explosive growth of GenAI and LLM. You may wonder which ones are really fundamental and worth picking up, you want to see case studies, and show what improvements they bring over traditional methods. Importantly, which ones are low-hanging fruits, easy to implement with the biggest bang for the buck.

I did my homework to help you navigate this ecosystem, and selected one video that covers quite a bit of really useful information. It covers:

  • KNN search in LLM contexts (embeddings)
  • ANN search (approximate KNN) using IVF and HNSW algorithms.
  • Retrieval Augmented Generation over Wikipedia articles about video games.
  • Hybrid queries using SQL.

Interestingly, it has a considerable overlap with what I am working on internally. This tutorial is targeted to developers, engineers and AI professionals keen to learn more about the technology and its implementation.

Watch the video here.


🔮 Fabrizio Degni

Chief of Artificial Intelligence |E.N.I.A. DTI| |LA ISO/IEC 42001&9001| C2PA Contributor |CAEGP|CCEM|MCE|CTU|IEEE WGM| eMBA Candidate |PhD Candidate |IEEE Region8 Ambassador |OpenEthics Ambassador |Gartner CM Ambassador

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