Qdrant: Improving search relevance with reranking & fusion 🚀

Kumar Shivendu (~KShivendu)


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Description:

Outline:

  • Intro to vector search and Qdrant
  • Some internals of Qdrant: HNSW Index
  • Why search or relevance matter in the age of GenAI and how to improve it
  • Re-ranking algorithms
  • Fusion algorithms

Takeaways:

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

Prerequisites:

  • Must have: Python, Basics of (keyword) search
  • Nice to have: Vector search

Content URLs:

Slides

Speaker Info:

I'm a Software Engineer at Qdrant (Open Source Vector DB written in Rust 🦀). I love tinkering with search, recommendations, data, RAG, and AI agents.

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