Scout’s 10M-token window enables single-pass multi-hop reasoning: retrieve all documents, solve multi-step queries without re-querying Milvus between hops.
Traditional RAG struggles with queries like “Which vendor from this contract review also appears in the compliance reports?” because truncation forces: (1) retrieve contracts, (2) extract vendors, (3) forget contracts, (4) retrieve compliance reports, (5) forget contract details, (6) make imprecise matches. Scout solves this: retrieve all contracts AND compliance reports in one Milvus query, and Scout processes both simultaneously, maintaining cross-document connections. The 10M window is large enough that all source material stays in-context—no forgetting between reasoning steps.
For Milvus, this changes query strategy. Instead of sequential retrieval (top-5 documents, process, re-query), use comprehensive retrieval (top-500 documents matching all aspects of the query). Scout’s mixture-of-experts routes different types of reasoning to appropriate experts as it synthesizes. This is why Scout is trending for agentic workflows: it supports complex multi-step reasoning without agentic loops. Combine with Milvus metadata filtering to pre-filter 1M documents to 500 candidates, then let Scout reason over all 500 at once.
Related Resources
- Agentic RAG with Milvus and LangGraph — multi-hop query patterns
- Enhance RAG Performance — complex reasoning optimization
- RAG with LlamaIndex — query routing strategies