Semantic Search

Semantic Search is an information retrieval approach that understands the meaning and context of search queries rather than relying solely on keyword matching. By using embeddings to represent both queries and documents as vectors, semantic search can find conceptually relevant results even when exact terms don't match.

Unlike traditional lexical search (like Elasticsearch's BM25), semantic search excels at: understanding synonyms and related concepts, handling natural language queries, finding relevant content despite vocabulary differences, and matching user intent to content meaning. Hybrid approaches combining semantic and keyword search often provide the best results.

Semantic search is fundamental to modern AI applications, particularly RAG systems. Implementation involves: choosing embedding models, selecting vector databases, tuning similarity thresholds, handling diverse content types, and optimizing for latency. Engineers should understand both the power and limitations of semantic search, including challenges with highly specific or technical queries.