177: Vector Databases

Intro topic: Buying a Car News/Links: Cognitive Load is what Matters https://github.com/zakirullin/cognitive-load Diffusion models are Real-Time Game Engines https://gamengen.github.io/ Your Company Needs Junior Devs https://softwaredoug.com/blog/2024/09/07/your-team-needs-juniors Seamless Streaming / Fish Speech / LLaMA Omni Seamless: https://huggingface.co/facebook/seamless-streaming Fish: https://github.com/fishaudio/fish-speech LLaMA Omni: https://github.com/ictnlp/LLaMA-Omni Book of the Show Patrick: Thought Emporium Youtube https://youtu.be/8X1_HEJk2Hw?si=T8EaHul-QMahyUvQ Jason: Novel Minds https://www.novelminds.ai/ Patreon Plug https://www.patreon.com/programmingthrowdown?ty=h 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 ★ Support this podcast on Patreon ★