Design an AI-Powered Semantic Search Engine — System Design Interview Practice
Design a search engine that understands natural language queries, uses vector embeddings for semantic similarity, supports hybrid search with keyword matching, and provides relevance ranking. Work through the requirements, architecture trade-offs, and an interactive design review.
Requirements and concepts to consider
- Convert documents into vector embeddingsRequirement
- Support semantic similarity searchRequirement
- Implement hybrid search (vector + keyword)Requirement
- Handle multi-modal search (text, images)Requirement
- Provide faceted filtering and aggregationsRequirement
- Support real-time index updatesRequirement
- Implement query understanding and expansionRequirement
- Rank results by relevanceRequirement
- aiConcept to explore
- searchConcept to explore
- embeddingsConcept to explore
- vector databaseConcept to explore
- nlpConcept to explore
- retrievalConcept to explore