The combination of vector databases and embedding models is central to modern Retrieval-Augmented Generation (RAG) systems. A recent Chinese tutorial showcases how to build a knowledge base using Milvus, an open-source vector database, and Tongyi Embedding, Alibaba's embedding model. This integration is particularly relevant for developers targeting Chinese-language content or leveraging Alibaba Cloud's ecosystem. The tutorial covers data preprocessing, embedding generation, vector storage, and querying—a standard RAG pipeline. For overseas developers, the key takeaway is the growing maturity of Chinese AI infrastructure: Tongyi Embedding offers competitive performance for Chinese text, and Milvus provides scalable vector search. This pattern is useful for multilingual knowledge bases, cross-border e-commerce, or any application requiring semantic search in Chinese. The article also implicitly compares with alternatives like OpenAI embeddings and Pinecone, making it a valuable reference for choosing the right stack for China-focused RAG projects.
This article demonstrates how to build a knowledge base using Milvus vector database and Alibaba's Tongyi Embedding model, a common RAG pattern. It highlights the growing ecosystem around Chinese AI models and vector databases, offering insights for developers building multilingual or China-focused RAG systems.