Proximity Search Algorithms (HNSW, IVFFlat)
Understand how Approximate Nearest Neighbor (ANN) algorithms like HNSW and IVFFlat enable fast similarity searches in high dimensions.
Finding Similar Vectors Fast
Welcome back! In our previous lessons, we learned about storing numerical representations (embeddings) of text or images in vector databases. But how do we actually find the *closest* or most similar vectors to a given query vector quickly?
This is where Proximity Search Algorithms come in! They are the secret sauce for super-fast similarity searches.
The Slow Way: Exact Search
The simplest way to find the closest vector is through a 'brute-force' approach, also known as Exact Nearest Neighbor (NN) search.
- It means comparing your query vector to every single other vector in the database.
- Imagine having billions of vectors! This would be incredibly slow and resource-intensive, making real-time applications impossible.
We need a much faster solution for large datasets!
All lessons in this course
- Vector DB Storage Architectures
- Proximity Search Algorithms (HNSW, IVFFlat)
- Vector DB Persistence and Scalability
- Quantization and Compression of Vectors