Day 2 (Embeddings and Vector Stores/Databases)
Today you will learn about the conceptual underpinning of embeddings and vector databases and how they can be used to bring live or specialist data into your LLM application. You’ll also explore their geometrical powers for classifying and comparing textual data.
1. Complete Unit 2: “Embeddings and Vector Stores/Databases”, which is:
1. Listen to the summary podcast episode for this unit (created by NotebookLM).
https://youtube.com/watch?v=1CC39K76Nqs
3, Complete these code labs on Kaggle:
1. Build a RAG question-answering system over custom documents ここまでやったtakker.icon
とりあえず動かした
質問でベクトル検索して見つけたdocsも併せてLLMに聞いて答えさせるサンプルだった 2. Explore text similarity with embeddings やったtakker.icon
テキストの類似度を計算し、heatmapとかで可視化するサンプル
3. Build a neural classification network with Keras using embeddings browserでも実行できるかな
2. Watch the YouTube livestream recording.
https://www.youtube.com/watch?v=86GZC56rQCc&list=PLqFaTIg4myu-b1PlxitQdY0UYIbys-2es&index=2