exa-web-search

To get current web information for research, you can use neural search to query the web and retrieve up-to-date results. This approach bypasses static training data by fetching live facts, returning structured results with titles, URLs, and highlights.

Yes, you can perform semantic web searches for topics lacking exact keywords by switching to neural search mode. This allows the system to understand the contextual meaning of your query rather than relying solely on strict keyword matching.

You can refine multi-step web searches by adjusting configurable parameters like result count, search mode, and domain filters. If initial information retrieval results are insufficient, applying these constraints helps narrow the semantic lookup for better outcomes.

Yes, neural web search returns structured results complete with citations and source URLs. The search output includes titles, highlights, and optional summaries, allowing you to synthesize a clear answer with verifiable links.

The best way to discover current information about specific companies or topics is using a neural search endpoint configured for auto mode. This automatically detects the optimal retrieval strategy, returning targeted web results with relevant highlights.

You should use keyword mode instead of neural mode for web information retrieval when you need to match exact terms within specific domains. Neural mode is better for semantic context, while keyword mode targets precise phrase matches.