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:
Concept-Based Querying
Users can pose questions or use natural language.
The system understands the intent behind the query.
Vector Embeddings
Queries and documents are transformed into high-dimensional vector spaces.
These spaces capture semantic relationships between words and concepts.
Similarity Algorithms
K-Nearest Neighbors (KNN) or other vector similarity algorithms are employed.
Cosine similarity (angle between vectors) is a common measure.
Query-Document Matching
The query's vector is compared to indexed document vectors.
Documents with the closest vector representations are returned.
Scoring Mechanism
Each returned document is assigned a relevance score.
Scores typically reflect the degree of semantic similarity to the query.
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Semantic Search
