Hands-on with Qwen 3 and Milvus: Building RAG with the Latest Hybrid Inference Models

This post details building a local, cost-aware Retrieval-Augmented Generation (RAG) system by integrating the Qwen 3 hybrid inference models with Milvus. It provides hands-on code for setting up the environment, preparing data from Milvus documentation, configuring Milvus Lite for local storage, and using the OpenAI-compatible API for Qwen 3 models. The tutorial covers creating a Milvus collection, generating embeddings for text chunks using `pymilvus.model.DefaultEmbeddingFunction`, inserting data, and performing semantic searches to build the RAG query system. It demonstrates how to retrieve relevant documents for a given question and outlines the process of using these retrieved documents to augment the Qwen 3 model's response.