Testing out the RAG feature Cloudflare launched this year, I just realized that the default general embedding model (vector model) bge-m3 was actually developed by a Chinese organization: 'Beijing Academy of Artificial Intelligence' (BAAI). This means the model strikes a great balance between computational efficiency and performance. On top of that, besides the bge series, another more powerful (but more computationally intensive) open-source option is Alibaba's Qwen3-Embedding 8B model. This means that in the field of open-source embedding/vector models, the default top choices are all from Chinese teams. However, in the realm of closed-source embedding models, OpenAI and others also have their unique advantages. One last note: embedding models are typically the retrieval layer that comes before LLMs/Agents—they first use vector similarity to gather relevant information, which is then handed over to large language models for reasoning and generation. So, they play a crucial role in many AI workflows.