exa

Semantic search finds conceptually related content by understanding meaning and context rather than relying on exact keyword matching. It leverages neural search to surface documents that share conceptual similarity with your query.

You can search for code context by invoking the code context search tool to locate programming patterns and API usage. It returns structured results that help you discover relevant code snippets and implementation examples.

Yes, semantic search supports company research by surfacing competitive and business information across online sources. You can gather market insights and corporate intelligence using the dedicated company research tool.

Yes, Exa web search supports optional domain and date filters to narrow results. You can also use autoprompt and result-type controls to refine the structured output returned by the search.

The best way to use neural search for academic research is to query broad topics and let the semantic engine surface conceptually related documents. This approach finds relevant literature even when exact terminology differs.

Semantic search for code exploration relies on neural matching rather than exact syntax indexing, so it may not surface every instance of a specific function name. It is best suited for discovering patterns and API usage contextually.