RAG with Real-Time Web Data

Build a Retrieval-Augmented Generation pipeline that uses live web data instead of static documents.

Design the pipeline

Plan your RAG architecture with real-time web search as the retrieval layer.

Use SwiftSearch and DeepDive to fetch relevant content.

const research = await client.research({
  topic: 'machine learning trends 2025',
  max_sources: 10
});

Process the retrieved content into embeddings for your vector store.

Use the retrieved context to generate accurate, cited responses.

Link back to source URLs for transparency and trust.

Get your free API key and start building in minutes.