Yes—Milvus supports real-time insertion and auto-indexing, while Scout processes newly-indexed documents immediately without retraining.
Scenario: ingest 1000 new documents/day (news, research papers, emails). Milvus batch inserts and incrementally updates indices within seconds. Scout queries immediately—no waiting for full index rebuild. The open-weights approach means no model retraining needed for new knowledge: update Milvus embeddings and Scout retrieves updated content instantly. This is critical for time-sensitive RAG (news analysis, security incident response) where knowledge cutoff matters.
For scalable ingestion: (1) use Milvus’s upsert API to update/insert documents, (2) embed with a fast model (BGE-small for speed, BGE-large for accuracy), (3) batch embeddings (1000 at a time) to amortize API calls, (4) partition by date/category in Milvus for faster pruning. Scout’s inference is stateless—each query is independent, so no batch processing required. Monitor embedding freshness: if docs are added hourly but embeddings computed daily, Scout answers questions against stale data. Use change-data-capture (CDC) or webhooks to embed documents immediately upon arrival.
Related Resources
- Milvus Quickstart — real-time data ingestion patterns
- Enhance RAG Performance — ingestion and indexing optimization
- Milvus Blog — real-time RAG case studies