Definition
Search-augmented RAG is a retrieval-augmented generation architecture that supplements vector database retrieval with live search API calls, ensuring the LLM context includes both stored knowledge and real-time web data.
In Depth
Standard RAG pipelines retrieve context from pre-indexed vector databases, which means the data is only as fresh as the last indexing run. Search-augmented RAG adds a parallel retrieval path through a live search API, so the context window includes both curated internal documents and current web results. This hybrid approach is especially valuable for queries about pricing, current events, product launches, or anything that changes frequently. The architecture typically works in three steps: (1) the query is sent to both the vector store and a search API in parallel, (2) results are merged and re-ranked by relevance, (3) the combined context is passed to the LLM for generation. Search APIs differ in what they return -- Exa provides semantic search results (1K free/mo, $5/1K after), Tavily returns summarized content ($0.008/query), and Scavio returns full structured SERP data ($0.005/credit) including knowledge graphs, PAA, and multi-platform results that provide richer grounding context.
Example Usage
A legal research tool combines a Pinecone vector store of case law with Scavio's live Google search API. For the query 'latest GDPR enforcement actions 2026', the vector store provides background context while the search API returns this week's rulings, producing a response that is both comprehensive and current.
Platforms
Search-Augmented RAG is relevant across the following platforms, all accessible through Scavio's unified API:
- YouTube
Related Terms
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Semantic Search vs Keyword Search
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