The primary difference lies in how the two approaches interpret and retrieve data. Keyword search matches exact terms or phrases in a dataset, relying on literal matches. For example, searching "blue car" will return documents containing "blue" and "car" but may miss synonyms like "azure automobile." In contrast, vector search analyzes the semantic meaning, enabling it to find contextually relevant results even if the exact keywords are absent.
Keyword search is rule-based and works well for structured data or situations requiring exact matches. However, it struggles with ambiguity, synonyms, or contextual nuances. Vector search, on the other hand, transforms data into embeddings—dense vector representations that capture semantic relationships. These embeddings allow it to locate items based on meaning rather than mere textual overlap. For instance, "buy a shirt" and "purchase clothing" might produce similar vector representations, resulting in relevant retrievals.
Developers use vector search in scenarios where meaning matters more than exact matches. Common use cases include retrieving similar images, question-answering systems, and multimedia search engines. Keyword search remains effective for traditional databases and structured queries, while vector search excels in unstructured data environments, offering a deeper, more nuanced understanding of content.