Vector Database Search - Hierarchical Navigable Small Worlds (HNSW) Explained

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In this video, we explore how the hierarchical navigable small worlds (HNSW) algorithm works when we want to index vector databases, and how it can speed up the process of finding the most similar vectors in a database to a given query.

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*Contents*
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00:00 - Intro
00:17 - Vector database and search
01:42 - Navigable small worlds
03:29 - Skip linked lists
04:49 - Hierarchical Navigable Small Worlds
06:47 - HNSW Search Speed
07:49 - Outro

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#vectordatabase #vectorsearch #rag #hnsw
  1. DataMListic
  2. deep learning
  3. machine learning
  4. artificial intelligence
  5. ml
  6. dl
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Комментарии
Автор

You have explained a complex topic in very simple terms. Keep up the good work.

Andies
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When initially building the small world we need to iteratively look for k nearest neighbors while inserting the new documents. How do we find those neighbors

Omarsayan
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In 6:37 there is an mistake because the "closest to query"-point was in Level 2 already there and not selected. Do you understand?

Lukas-ilxg
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Good Explanation Provided. Thank you vey much for this.

AkshayKadamIN
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Perfect explanation, exactly what I was looking for!

himanikumar
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great explanation, massively underrated video

ZoinkDoink
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Very good explanation with a good use of animations!

parth
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Very nicely explained! Thank you for making this video

emanuelgerber
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Hey, have a question, isnt there a risk of getting stuck in a local optima when comparing similarity between query node and db nodes in the graphs?

maheswaranparameswaran
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How did you go from a group of random vectors to a skip linked list structure?

lynnwilliam
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If the K equals to the total number of documents, will this approach also be like brute force? Because it needs to go through each linked document.

alicetang
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could u please come up with nlp bascis course please. also basics of ML course please.

saisaigraph