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Quiz: Embeddings and Search

5 questionsNo time limitPass: 60%+10 XP on first pass

How computers find text by meaning: keyword search vs semantic search, vectors and cosine similarity, a runnable search over a college handbook, how embedding models and vector databases (pgvector, Chroma) scale it up, and hybrid search.

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  1. Question 1

    What does a cosine similarity close to 1 mean?

  2. Question 2

    Why did "how many classes can I bunk" fail with TF-IDF search?

  3. Question 3

    You already run PostgreSQL. What's the simplest way to add vector search?

  4. Question 4

    Which query is better served by keyword search than semantic search?

  5. Question 5

    What should a vector database store with each vector?