How to Build RAG with Milvus, QwQ-32B and Ollama

The tutorial provides a detailed guide on building a Retrieval-Augmented Generation (RAG) system using QwQ-32B, Milvus, and Ollama. The RAG system combines the strengths of both dense models like QwQ-32B for deep reasoning tasks and embedding-based retrieval systems like Milvus for efficient data storage and retrieval. The tutorial outlines a step-by-step process on how to build such a pipeline, including preparing the necessary dependencies, loading data into Milvus, integrating QwQ-32B as the language model, and using Ollama for seamless deployment and management of large language models. By following this guide, developers can create RAG systems tailored to their specific needs, particularly beneficial for applications requiring real-time information retrieval and generation such as AI-assisted tutoring, logic-based problem-solving, and more.

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