What is Semantic Search?
Turkish: Semantik Arama
Semantic search finds relevant results by comparing the meaning of queries and content, not only matching exact keywords.
What is Semantic Search?
Semantic search considers intent and meaning instead of searching only for the exact words typed by the user. A query such as “how many days do I have for a refund?” can match a document that says “the withdrawal period is 14 days.”
The content is first converted into embedding vectors. The user query is converted into the same vector space, and the nearest vectors are retrieved from a vector database.
How It Works
The process usually includes data preparation, chunking, embedding generation, indexing, and similarity search at query time. Metadata filters such as language, category, date, or customer segment narrow the result set.
Semantic search is often not used alone. It can be combined with keyword search and then passed through reranking. This keeps exact term matches while also capturing meaning.
Business Use
Semantic search is useful for technical documentation, support articles, product catalogs, contract archives, and internal knowledge bases. In RAG systems, retrieving the right document directly affects the quality of the model answer.
A strong setup depends on more than model choice. Data cleaning, chunk size, update frequency, and evaluation datasets determine search quality.
Related Terms
An embedding represents text, images, products, or other data as numeric vectors that can be compared for semantic similarity.
Knowledge GraphA knowledge graph models entities such as people, products, documents, and processes with relationships that systems can query.
RAG (Retrieval-Augmented Generation)RAG is an AI architecture where a language model retrieves relevant passages from documents or databases before generating an answer.
RerankingReranking re-scores an initial result set with a stronger model so the most relevant documents move to the top reliably.
Vector DatabaseA vector database stores embeddings and retrieves records by semantic similarity, making it a core layer in AI search systems.