Use Case

AI Search

Building intelligent search with semantic understanding.

AI search goes beyond keyword matching to understand meaning. Semantic search finds relevant results even when exact terms don't match.

AI Search Components

Embeddings: Convert text to vectors
Vector database: Store and search vectors
Hybrid search: Combine semantic + keyword
Re-ranking: Improve result ordering

Search Quality Metrics

Precision: Relevant results / total results
Recall: Found relevant / all relevant
MRR: Mean Reciprocal Rank
NDCG: Normalized Discounted Cumulative Gain

Is semantic search better than keyword search?

For natural language queries, yes. Hybrid approaches combining both often work best. Test with your specific use case.

Monitor search quality