Integration
Semantic Search
Semantic search is a retrieval approach that finds documents by meaning rather than keyword overlap. A query and all candidate documents are encoded as embeddings, and results are ranked by vector similarity — so a search for 'heart failure treatment' returns results about 'cardiac management protocols' even without exact keyword matches.
Semantic search is the retrieval backbone of RAG pipelines and enterprise knowledge bases. Hybrid search — combining semantic and keyword (BM25) retrieval — outperforms either approach alone and is the production default for most enterprise search deployments in 2026.
Semantic Search in practice
Typical business uses: a staff assistant that finds the right policy clause, a support bot that matches a customer's wording to the right help article, and contract search across years of documents. Quality depends on how documents are split into passages, on access rights carried into the index and on a test set of real questions with known answers. Remolda builds semantic search into internal knowledge assistants and data pipelines for AI.
Related terms
- Embedding · Data
- Vector Database · Data
- RAG (Retrieval-Augmented Generation) · Data