Improving vector search relevance with reranking & fusion 🚀

Kumar Shivendu (~KShivendu)





  • Intro to vectors, vector search, and vector DBs
  • Vector search internals: HNSW Index
  • Why search (relevance) is critical for building great RAG applications
  • Re-ranking algorithms
  • Fusion algorithms


  • Understand vector search internals and its limitations
  • Insights into different models and algos used for reranking and fusion


  • Must have: Basics of python, search, and ML
  • Nice to have: Vector search, RAG

Content URLs:


Speaker Info:

I love tinkering with search, recommendations, and RAG. I have worked at multiple early stage startups like SuperTokens (YC 20) and FamPay (YC 19). I discovered my passion for search during my GSoC internship, where I built advanced search features on the top of metadata from 250M+ open source repos for the Software Heritage organization.

I currently work as a Software Engineer at Qdrant (OSS Vector Search Engine written in Rust 🦀) and I love to give deep technical talks to share my knowledge. I've given multiple talks in the past at meetups like FOSS United, Tensorflow User group, Microsoft Reactor, etc.

Section: Artificial Intelligence and Machine Learning
Type: Talk
Target Audience: Intermediate
Last Updated: