Semantic search with embeddings

How to encode queries and documents as vectors, retrieve the nearest candidates by meaning rather than exact words, and combine that with metadata filters, approximate search at scale, and keyword search where embeddings fall short.

Questions in this set

11 questions · 16 min read

01What is semantic search?
02How does text become a search vector?
03What similarity function should be used?
04Can we calculate and rank a tiny example?
05How do you build a basic semantic index?
06What is approximate nearest-neighbor search?
07How do metadata filters work with vectors?
08When does keyword search beat embeddings?
09What is hybrid search?
10How do embedding model changes affect an index?
11How do you test semantic search?

Tutor

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Semantic search with embeddings

Questions in this set

11 questions · 16 min read

01What is semantic search?
02How does text become a search vector?
03What similarity function should be used?
04Can we calculate and rank a tiny example?
05How do you build a basic semantic index?
06What is approximate nearest-neighbor search?
07How do metadata filters work with vectors?
08When does keyword search beat embeddings?
09What is hybrid search?
10How do embedding model changes affect an index?
11How do you test semantic search?

Tutor

Want me to explain it differently?

AI concepts can be dense. Tell me what's confusing and I'll find a new analogy.