How does vector search differ from traditional keyword search?

Vector search and traditional keyword search differ fundamentally in how they interpret and retrieve information. Keyword search relies on exact matches or predefined rules (like stemming or synonym expansion) to find documents containing specific terms. For example, a search for “python tutorial” might return pages where those exact words appear, or variations like “tutorials” if the system is configured for basic text expansion. Vector search, on the other hand, uses machine learning models to map data (text, images, etc.) into numerical representations called vectors. These vectors capture semantic meaning, allowing the system to find results based on conceptual similarity rather than literal keyword matches. For instance, a vector search for “python tutorial” could return content about “learning Python basics” even if the exact phrase isn’t present, because the vector representations of the query and the content are close in the model’s mathematical space.

The way each approach processes queries also differs. Keyword search systems often depend on inverted indexes and Boolean logic to filter results, which can struggle with ambiguous terms, typos, or context. For example, a search for “Java” might return irrelevant results about the island instead of the programming language unless the user adds clarifying terms. Vector search handles such ambiguity by analyzing the broader context. A query for “Java” in a technical context would align more closely with vectors representing programming concepts. Additionally, ranking in keyword search is typically based on metrics like term frequency or document structure (e.g., title tags), while vector search ranks results by measuring the distance between vectors (e.g., using cosine similarity), prioritizing items that are semantically related to the query.

Finally, vector search excels at handling unstructured data and complex relationships that keyword systems can’t address. For example, a developer searching for “code examples for handling API rate limits” might miss relevant articles that use phrases like “throttling REST requests” in a keyword system. A vector search would recognize the semantic overlap and surface those results. However, vector search requires more computational resources for indexing and querying and may lack the precision of keyword systems for exact matches. Hybrid approaches that combine both methods are common, using keyword filters for strict constraints and vector search for relevance ranking. Choosing between them depends on the use case: keyword search for precise, syntax-heavy tasks (e.g., log analysis), and vector search for conceptual exploration (e.g., research or recommendation systems).