177: Vector Databases
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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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