177: Vector Databases

Intro topic:  Buying a Car News/Links: Cognitive Load is what Mattershttps://github.com/zakirullin/cognitive-loadDiffusion models are Real-Time Game Engineshttps://gamengen.github.io/Your Company Needs Junior Devshttps://softwaredoug.com/blog/2024/09/07/your-team-needs-juniorsSeamless Streaming / Fish Speech / LLaMA OmniSeamless: https://huggingface.co/facebook/seamless-streamingFish: https://github.com/fishaudio/fish-speech LLaMA Omni: https://github.com/ictnlp/LLaMA-Omni  Book of the ShowPatrick: Thought Emporium Youtubehttps://youtu.be/8X1_HEJk2Hw?si=T8EaHul-QMahyUvQJason: Novel Mindshttps://www.novelminds.ai/ Patreon Plug https://www.patreon.com/programmingthrowdown?ty=h Tool of the ShowPatrick: Escape Simulatorhttps://pinestudio.com/games/escape-simulator/Jason: Cursor IDEhttps://www.cursor.com/ Topic: Vector Databases (~54 min) How computers represent data traditionallyASCII valuesRGB valuesHow traditional compression worksHuffman encoding (tree structure)Lossy example: Fourier Transform & store coefficientsHow embeddings are computedPairwise (contrastive) methodsForward models (self-supervised)Similarity metricsApproximate Nearest Neighbors (ANN)Sub-Linear ANNClusteringSpace Partitioning (e.g. K-D Trees)What a vector database doesPerform nearest-neighbors with many different similarity metricsStore the vectors and the data structures to support sub-linear ANNHandle updates, deletes, rebalancing/reclustering, backups/restoresExamplespgvector: a vector-database plugin for postgresWeaviate, Pinecone Milvus ★ Support this podcast on Patreon ★