Embedding

1. Introduction In the design of vLLM Semantic Router, embedding is one of the core signals driving semantic routing. The official tutorial has already demonstrated how to use Remote Embedding Providers to replace local embedding inference services: 👉 https://vllm-sr.ai/docs/tutorials/global/remote-embeddings/ However, in real production environments, a more critical question arises: How can we use enterprise-grade, scalable embedding services to support semantic routing? This article is based on the official tutorial scenario and combines Microsoft Foundry embedding models to demonstrate how to build a:...

In an AI architecture where multiple models coexist, such as GPT-4, GPT-4o, lightweight models, and vertical-domain models, one core question is: How can the system automatically select the most suitable model without explicitly specifying a model ID? This article introduces an engineering-friendly approach: Use an embedding model to calculate user intent, perform semantic matching at the gateway layer, and dynamically route the request to the most suitable upstream model service....