Advanced9 min
Router-Based RAG: Multi-Source Knowledge
Route queries to different retrieval sources based on classification — vector stores, SQL databases, APIs, or specialized indexes — for optimal answers from the right source.
Quick Reference
- →Router-Based RAG classifies the query first, then routes to the optimal retrieval source
- →Sources can include vector stores, SQL databases, APIs, graph databases, or web search
- →Use structured output classification to select the source with highest relevance probability
- →Fallback chains: if the primary source returns no results, try the secondary source
- →Multi-source fusion: query multiple sources in parallel and merge results
- →Critical for enterprise systems with heterogeneous data across multiple backends
Why Route Queries?
Classify query → route to the best data source → generate grounded answer
Most real-world knowledge bases aren't a single vector store. A company has product docs in Pinecone, customer data in PostgreSQL, financial reports as PDFs, and real-time data from APIs. Router-Based RAG classifies each query and sends it to the source most likely to have the answer — rather than searching everything and hoping for the best.
| Query Type | Best Source | Why |
|---|---|---|
| 'How do I configure OAuth?' | Vector store (docs) | Semantic search on documentation |
| 'How many users signed up last month?' | SQL database | Exact numerical query |
| 'What's the current stock price?' | API (real-time data) | Needs live data, not static docs |
| 'Who reports to the CTO?' | Graph database | Relationship traversal |
| 'What did the CEO say in Q3 earnings?' | Vector store (transcripts) | Semantic search on unstructured text |