Build RAG with LangChain, Milvus, and Strapi

This is a summary of the provided text: The Retrieval-Augmented Generation (RAG) system uses a combination of AI models, vector databases, and content management systems to provide accurate and relevant answers to user queries. The system consists of three main components: Milvus for vector storage, Strapi for content management, and LangChain for workflow coordination. The RAG system is designed to bridge the gap between generic AI responses and specialized knowledge by integrating a retrieval mechanism with the generation process. It uses OpenAI's GPT-3.5 model for generating responses and converts text into vectors using embeddings models. The system can be integrated with various tools and services, including Milvus vector store integration, Strapi content management, and LangChain workflow coordination. The RAG system is ideal for applications like customer support, knowledge management, and educational tools. It provides accurate and relevant answers grounded in real, up-to-date knowledge and can be tailored to specific needs with a clear understanding of the architecture and this step-by-step guide.