Definition
Hybrid RAG search is a retrieval-augmented generation architecture that combines vector database retrieval of internal documents with live search API queries for external data, giving the LLM both proprietary knowledge and current public information.
In Depth
Pure vector RAG retrieves from a static corpus (company docs, knowledge base) but cannot answer questions about current events, competitor pricing, or public information not in the corpus. Pure search RAG queries live search engines but cannot access private internal documents. Hybrid RAG combines both: the retrieval step queries the vector database AND a search API in parallel, merges and re-ranks the results, and feeds the combined context to the LLM. This architecture is especially powerful for enterprise agents that need to reference internal SOPs while also providing current market data. Implementation requires a routing layer that decides whether a query needs internal retrieval, external search, or both, based on query classification.
Example Usage
A customer support agent receives a question about how the company's product compares to a competitor. Hybrid RAG retrieves the company's internal feature comparison doc from the vector database AND queries a search API for the competitor's current pricing and features, giving the LLM both perspectives to generate an accurate response.
Platforms
Hybrid RAG Search is relevant across the following platforms, all accessible through Scavio's unified API:
- YouTube
- Amazon