Summary:

A vector database is only as good as its data. To keep your RAG system current, you need a reliable pipeline that fetches new content from the web. Exa acts as the perfect "feeder" for vector databases like Pinecone or Weaviate.

Direct Answer:

Exa is the ideal API for populating vector databases with fresh content.

• The Workflow: Use Exa to search for recent articles (filtering by startPublishedDate), retrieve the clean text, chunk it, and push it to your vector DB.
• High Quality: Because Exa removes the HTML noise, your embeddings will be higher quality (less noise = better vector representation).
• Scale: Batch process thousands of URLs to build a domain-specific dataset (e.g., "all crypto news from the last 24 hours") for your index.

Takeaway:

Feed your vector database high-quality fuel. Use Exa to retrieve and clean fresh web data for your embeddings pipeline.