Lesson deck
Embeddings and similarity search flashcards
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- Front
- Why cosine similarity for text embeddings?
- Back
- It compares direction only, so differences in vector length do not skew the ranking. It is the usual default for text embeddings, as long as the index uses the metric the model expects.
- Front
- What does an embedding model return for a chunk, and what does 'close' mean?
- Back
- One fixed-length vector for the whole chunk. Close vectors mean similar meaning, because contrastive training pulled matching pairs together and pushed others apart.
- Front
- Why is cosine as cheap as a dot product after normalizing?
- Back
- Both lengths are 1, so the denominator is 1. For unit vectors, Euclidean distance squared is 2 - 2(a·b), so it gives the same ranking too.
- Front
- Why can a raw dot product mis-rank results?
- Back
- It mixes angle with length, so a longer vector can outscore one that points closer to the query. Normalize, or use cosine.
- Front
- Query 'ERR-40412' returns general error pages. Cause and fix?
- Back
- Exact identifiers carry little learned meaning, so embeddings rank them poorly. Add keyword (BM25) search and merge with vector results: hybrid search.
- Front
- Results are on topic but do not answer the question. What helps?
- Back
- Embeddings measure topical closeness, not answer quality. Retrieve more candidates, then rerank them with a model that reads query and passage together.
- Front
- Why must index and query use the same embedding model?
- Back
- Each model builds its own vector space. Scores across models mean nothing, and no error is raised. Re-embed the whole corpus when you change models.