1.3 Embeddings & Semantic Search #3
Description
Goal
Build semantic search using embeddings and compare with lexical search.
Learn
- How text embeddings work (conceptually)
- Vector similarity (cosine, dot product, euclidean)
- When semantic beats lexical and vice versa
- Hybrid search (combining both)
Deliverable
- Semantic search using OpenAI/Cohere embeddings
- Side-by-side comparison: BM25 vs semantic vs hybrid
- Query analysis showing when each wins
Proof Point
Can explain when each approach wins and why.
Directory
search-fundamentals/03-embeddings-similarity/