Deploy Milvus on Kubernetes or Docker, serve Scout via vLLM or Ollama, and route Milvus search results to Scout with LangChain or LlamaIndex integrations.
Starting architecture: (1) Milvus cluster handles vector storage and semantic search, (2) embedding model converts documents and queries to vectors, (3) vLLM or similar serves Scout with quantization for GPU efficiency, (4) LlamaIndex orchestrates the flow—query → embed → Milvus retrieve → Scout generate. For fault tolerance, add request queuing (Celery or Cloud Tasks), monitoring (Prometheus + Grafana), and rate limiting. Scout’s sparse MoE helps here: 10M-token context with only 17B active params means lower GPU memory, enabling cheaper infrastructure than dense alternatives.
Optimization tips: (a) batch documents in Milvus before inserting (faster indexing), (b) enable quantization on Scout (4-bit or 8-bit reduces memory 4x with minimal quality loss), © use metadata filtering in Milvus to pre-filter by date/category before semantic search, (d) cache Scout’s router weights across requests. For agentic workflows where Scout makes decisions to re-query Milvus, add LangGraph for state management. Milvus’s Python SDK and REST API integrate seamlessly with both approaches.
Related Resources
- Milvus Quickstart — setup in production environments
- Agentic RAG with Milvus and LangGraph — production agentic patterns
- RAG with LlamaIndex — orchestration and integration