k-NN Vector Search Breakdown #ai #tutorial #tech

Preporato | AI for Engineers · Beginner ·🔍 RAG & Vector Search ·3mo ago

About this lesson

This video demonstrates the k-NN algorithm, showing how a query vector navigates an embedding space. We visualize a breadth first search expansion to find relevant neighbors, culminating in a detailed similarity search. The process highlights efficient information retrieval within vector databases.

Original Description

This video demonstrates the k-NN algorithm, showing how a query vector navigates an embedding space. We visualize a breadth first search expansion to find relevant neighbors, culminating in a detailed similarity search. The process highlights efficient information retrieval within vector databases.

Watch on YouTube ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

Related AI Lessons

Your AI Keeps Making Things Up. RAG Is How You Make It Use Real Facts Instead.

Learn how to use RAG to make your AI provide accurate answers based on real facts instead of making things up

Evaluation Metrics for RAG: Measure Retrieval, Generation, and End-to-End Quality With Numbers That…

Learn to evaluate RAG models using metrics that measure retrieval, generation, and end-to-end quality

Evaluation Metrics for RAG: Measure Retrieval, Generation, and End-to-End Quality With Numbers That…

Learn to evaluate RAG models using metrics that measure retrieval, generation, and end-to-end quality

Medium · Data Science

When Does HyDE Help RAG? I Tested 3 Query Types and It Failed on Two

Learn when HyDE retrieval helps or hinders RAG performance across different query types, and why it matters for improving search accuracy

RRF vs DBSF with Qdrant: Hybrid Retrieval Fusion for RAG in Python

Professor Py: AI Engineering