Skip to content

resource

Embeddings and Vector Search: Exercise Guide

Study embeddings and vector search in Chapter 6. Choose the student notebook for construction or repair, check setup and retain your own attempt.

Available draft · two ninety-minute units · construction edition

Attempt the student work before consulting the solutions. Untouched exercises intentionally report NEEDS_WORK.

SessionNotebookMatching textPurpose
A · 90 minutesA: embeddings and a vector storeMarkdownConstruct, connect and explain
B · 90 minutesB: evaluate and repair retrievalMarkdownDiagnose, repair and transfer

Each notebook includes its own setup, first-principles introductions and supplied data, and needs only Python's standard library: no model, download or key. Unit B ships a real embedding model's vectors for its questions. Open either notebook in Google Colab with its badge (Colab runs Python 3.12), or use a local Python 3.12+ Jupyter kernel. No repository checkout, previous notebook kernel or live account is needed. Unit B can use an explicitly selected successful Unit A handoff; an invalid selected file refuses rather than substituting an answer.

Ninety minutes is the planned work allowance per unit. Actual completion time and understanding require classroom observation.

Setup · Back to this asset

Keep building with Prof Rod

Found this material through a colleague, classroom or shared download? Get the complete book at profrod.ai/book and join the Prof Rod learner community. Bring one result, one question or one failure you learned from. Share this resource with another learner and keep its source links with it so they can find the full course and future updates.