Semantic search operates on the conceptual meaning of a query rather than simple string matching. This approach allows for more intelligent and context-aware results. Key Features of Semantic Search:
Users can pose questions or use natural language. The system understands the intent behind the query. Queries and documents are transformed into high-dimensional vector spaces. These spaces capture semantic relationships between words and concepts. K-Nearest Neighbors (KNN) or other vector similarity algorithms are employed. Cosine similarity (angle between vectors) is a common measure. The query's vector is compared to indexed document vectors. Documents with the closest vector representations are returned. Each returned document is assigned a relevance score. Scores typically reflect the degree of semantic similarity to the query. 
Semantic Search
