Semantic search retrieves information by estimating the meaning and intent of a query rather than matching only its exact words. In AI applications, it commonly compares query embeddings with document embeddings so passages using different vocabulary can still rank as relevant.
Google Cloud’s semantic search overview describes the role of language understanding and context. In a private corpus, the implementation usually begins with embeddings.
How does semantic search work?
The system converts a query and each candidate passage into vectors. It asks a vector database for nearby points and returns the associated text. Similarity is a useful retrieval signal, not proof that a passage answers the question, so relevance still needs evaluation.
When should semantic search be combined with keywords?
Use hybrid search when the corpus contains identifiers, names, dates, or domain vocabulary that exact matching handles better. A reranking step can then score the best candidates more carefully. For RAG, the right question is whether the retrieved passage supports the answer, not whether the vector score looks high.
Frequently asked questions
Is semantic search the same as vector search?
Vector search is a mechanism for finding nearby vectors. Semantic search is the user-facing goal of matching meaning and may use vectors, language models, knowledge graphs, or a combination.
When does semantic search perform poorly?
It can struggle with exact product codes, rare names, dates, or specialized terms that the embedding model does not represent well. Hybrid search can recover those matches.
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