
177: Vector Databases
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About this listen
Intro topic: Buying a Car
News/Links:
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- https://github.com/zakirullin/cognitive-load
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- Patrick:
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- https://youtu.be/8X1_HEJk2Hw?si=T8EaHul-QMahyUvQ
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- Jason:
- Novel Minds
- https://www.novelminds.ai/
- https://www.novelminds.ai/
- Novel Minds
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- Jason:
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- Cursor IDE
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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