With a traditional lexical-based (or keyword-based) search, we will find documents that contain the exact word we searched for. Keyword search excels at precision but struggles with alternate phrasing or natural language. Semantic search addresses these limitations by capturing the intent behind documents and user queries. This is typically done by leveraging vector embeddings to map documents and queries into a high dimensional space and computing vector similarity to retrieve relevant results.
