Programming Throwdown

177: Vector Databases


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News/Links:

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    • https://github.com/zakirullin/cognitive-load
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    • https://gamengen.github.io/
  • Your Company Needs Junior Devs
    • https://softwaredoug.com/blog/2024/09/07/your-team-needs-juniors
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Book of the Show

  • Patrick: 
    • Thought Emporium Youtube
      • https://youtu.be/8X1_HEJk2Hw?si=T8EaHul-QMahyUvQ
  • Jason: 
    • Novel Minds
      • https://www.novelminds.ai/


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Tool of the Show

  • Patrick: 
    • Escape Simulator
      • https://pinestudio.com/games/escape-simulator/
  • Jason: 
    • Cursor IDE
      • https://www.cursor.com/

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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