← AI Terminology
Semantic Search
Semantic search retrieves documents by meaning using embeddings and similarity, rather than only exact keyword matches.
It powers modern RAG, enterprise search, and recommendation-like retrieval.
It powers modern RAG, enterprise search, and recommendation-like retrieval.
Why It Matters in AI
Users ask with different words than documents use. Semantic search finds relevant content despite paraphrase — transformative for knowledge bases, but weak alone on exact IDs and rare terms (hence hybrid search).
Key Points
| Aspect | Description |
|---|---|
| Eval | nDCG, recall@k on labeled queries |
| Models | Sentence transformers, proprietary embedding APIs |
| Related | BM25, hybrid search, rerankers |
| Pipeline | Embed query → ANN over doc embeddings → top-k |
| Strength | Paraphrase and conceptual match |
| Weakness | Exact SKUs, names, legalese keywords |
Simple Analogy
Finding a café when you search “somewhere cozy to read” even if no listing uses the word cozy — matching intent, not strings.
Common Usage Examples
SentenceTransformer.encode+ cosine top-k- Azure/OpenAI/Cohere embeddings search
- Intranet RAG search boxes
- Compare to Elasticsearch BM25-only
Summary
In short: Semantic search finds content by meaning via embeddings — the retrieval style that makes RAG work across paraphrases.