Embeddings and Semantic Search: How Context-Aware AI Works
Citai Team
March 6, 2026 · 9 min read
What are embeddings?
An embedding is the numerical representation of a text’s meaning. Each fragment becomes a 384-dimension vector capturing its semantic meaning.
Texts with similar meaning have nearby vectors. Different texts are far apart.
Why isn’t keyword search enough?
Vocabulary mismatch: If your document says “subscription cancellation” and users search “how to unsubscribe”, keyword search finds nothing. Embeddings understand both phrases mean the same thing.
Hybrid search: best of both worlds
Embeddings fail with codes (“ERR_429”), proper nouns, and acronyms. Citai combines embeddings + BM25 + RRF fusion.
Semantic cache: embeddings to save costs
Citai implements production-grade multilingual semantic search. Try it free →
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