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Topic: Vector Databases (~54 min)

How computers represent data traditionally
- ASCII values
- RGB values
How traditional compression works
- Huffman encoding (tree structure)
- Lossy example: Fourier Transform & store coefficients
How embeddings are computed
- Pairwise (contrastive) methods
- Forward models (self-supervised)
Similarity metrics
Approximate Nearest Neighbors (ANN)
Sub-Linear ANN
- Clustering
- Space Partitioning (e.g. K-D Trees)
What a vector database does
- Perform nearest-neighbors with many different similarity metrics
- Store the vectors and the data structures to support sub-linear ANN
- Handle updates, deletes, rebalancing/reclustering, backups/restores
Examples
- pgvector: a vector-database plugin for postgres
- Weaviate, Pinecone
- Milvus

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