vLLM Semantic Router + Milvus: How Semantic Routing and Caching Build Scalable AI Systems the Smart Way

What this post added

This post introduces the integration of Milvus as a semantic cache layer with the vLLM Semantic Router. It details how Milvus's distributed vector database capabilities enable efficient semantic caching for AI systems by storing and retrieving embeddings, thereby improving scalability, reducing latency, and lowering costs. The post explains the architecture of semantic routing, mixture of models, and the cache layer, emphasizing Milvus's role in handling large-scale semantic search with ANN algorithms. It also provides a practical guide for developers to test this integration locally, demonstrating the caching mechanism for AI applications.