Implement semantic search to improve class discovery. Instead of keyword-only matching, use vector embeddings to understand user intent and return relevant classes based on meaning.
Requirements
Generate embeddings for class data (title, description, tags, instructor)
Store vectors and perform similarity search
Rank results by semantic relevance
Integrate with existing search
Acceptance Criteria
Natural language queries return relevant classes
Results ranked by semantic similarity
Graceful fallback to keyword search if unavailable
Implement semantic search to improve class discovery. Instead of keyword-only matching, use vector embeddings to understand user intent and return relevant classes based on meaning.
Requirements
Generate embeddings for class data (title, description, tags, instructor)
Store vectors and perform similarity search
Rank results by semantic relevance
Integrate with existing search
Acceptance Criteria
Natural language queries return relevant classes
Results ranked by semantic similarity
Graceful fallback to keyword search if unavailable