Hands-on RAG with Qwen3 Embedding and Reranking Models using Milvus

What this post added

This post introduces the integration of Qwen3 embedding and reranking models with Milvus to build a RAG system. It details the setup of Qwen3 embedding and reranking models, including their multilingual capabilities, instruction prompting, variable dimensions, and context length. The post provides a practical implementation of a two-stage retrieval pipeline: dense retrieval using Qwen3 embeddings (with query-specific prompting) and reranking using Qwen3 cross-encoder. It also demonstrates data preparation, loading into Milvus Lite, and performing searches and reranking. The use of Milvus's Inner Product (IP) metric and strong consistency level is highlighted.