Semantic search uses embedding models to find results based on conceptual similarity rather than literal keyword overlap. A keyword search for "heart attack" won't return documents that only say "myocardial infarction." Semantic search finds both because their embeddings are close in vector space.
This is the core retrieval mechanism in RAG. You embed the user's query, search the vector index for the most similar document chunks, and surface those as context. The quality ceiling is your embedding model — weak embeddings mean semantically similar text lands far apart in the index.
Semantic search shines for natural-language queries, question answering, and cross-lingual retrieval. It struggles with exact matches, codes, and proper nouns — which is why hybrid search combining semantic and keyword lookup consistently outperforms either alone.
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