Semantic Search — Replace Keywords With AI Understanding

I will implement semantic search for your website, application, or content platform — replacing keyword matching with embedding-based semantic similarity search that finds conceptually relevant results even when the query and content use different words. Semantic search's value is most visible on the queries that keyword search completely fails — a user searching "how to cancel my subscription" should find your cancellation policy page even if it's titled "Membership Management"; semantic understanding finds the relevant content that keyword matching misses whenever user vocabulary doesn't perfectly match content vocabulary.

The implementation covers content embedding generation, vector store setup, a semantic search API endpoint, a search results interface replacing your existing search, relevance threshold configuration, and quality evaluation comparing semantic against keyword search on your query set.