Represent meaning for retrieval
Learn how embeddings and indexes support semantic search.
- Describe embeddings, similarity search, and the limits of semantic matching.
An embedding maps text to a numeric vector. Semantic retrieval compares a query vector with document chunk vectors, often using a distance or similarity measure. Similar vectors can indicate related meaning, but similarity is not proof that a passage answers the question. Keyword search can complement semantic search when exact names, codes, or phrases matter.
A small example
scores = {"setup guide": 0.91, "billing policy": 0.42}
ranked = sorted(scores.items(), key=lambda item: item[1], reverse=True)
print(ranked[0][0])setup guide
The embedding model used for queries should be compatible with the one used to index documents. Indexes make nearest-neighbor lookup practical at scale. Consider freshness, filters, and exact-match needs as well as vector similarity.
Key takeaways
Describe embeddings, similarity search, and the limits of semantic matching.
Check that retrieved evidence is relevant, current, and allowed for this user.
Lesson quiz
5 questions · pass with 4 correct · up to 50 XP
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