Metadata Filtering for Vector Search + Latest Filter Tech
Vector similarity search makes massive datasets searchable in fractions of a second. Yet despite the brilliance and utility of this technology, often what seem to be the most straightforward problems are the most difficult to solve. Such as filtering.

Filtering takes the top place in being seemingly simple — but actually incredibly complex. Applying fast-but-accurate filters when performing a vector search (ie, nearest-neighbor search) on massive datasets is a surprisingly stubborn problem.

This article explains the two common methods for adding filters to vector search, and their serious limitations. Then we will explore Pinecone’s solution to filtering in vector search.

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🌲 Pinecone article:
https://www.pinecone.io/learn/vector-search-filtering/

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00:00 Intro
00:24 Vector Search Recap
02:03 Why Filter?
02:56 Metadata Filtering 101
07:48 Pre-filtering
09:37 Post-filtering
11:30 Single-Stage Filtering
12:22 Vectors and Metadata Code
13:58 Connecting to Pinecone
14:55 Building Query Vector
16:47 Querying
21:37 First Filter
24:40 Adding More Conditions
27:03 Filtering with Numbers
30:55 Search Speed and Filtering
33:44 Outro