What is Semantic Search? - Definition & Meaning
Learn what semantic search is, how searching by meaning works instead of keywords, and why it works better for knowledge bases and AI.
Definition
Semantic search is a search technique that finds results based on the meaning or intent of a query, rather than exact keyword matches. It understands synonyms, context, and conceptual relationships.
Technical explanation
Semantic search uses embeddings to represent queries and documents as vectors. Similarity (cosine, Euclidean distance) determines relevance. Vector databases (Pinecone, Weaviate, pgvector) index embeddings for fast nearest-neighbor search. Modern semantic search often combines with keyword/BM25 for hybrid search. Embedding models (Sentence-BERT, OpenAI text-embedding) learn semantic representations. Semantic search is the foundation for RAG retrieval and enterprise search solutions.
How AVARC Solutions applies this
AVARC Solutions implements semantic search in knowledge bases, document portals, and RAG systems. We use embeddings and vector databases to let users search in natural language and find relevant content regardless of exact wording.
Practical examples
- An internal knowledge base where "how do I reset my password" also finds documents about "recover login credentials" and "account recovery".
- An e-commerce search where "comfortable summer dress" finds products with "breathable dress", "casual summer dress" and related descriptions.
- A RAG system semantically retrieving the most relevant document chunks for an LLM answer.
Related terms
Frequently asked questions
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