📄 中文摘要
在加拿大与中国的 B2B 贸易领域，全球买家难以找到合适的中国供应商，而中国供应商也缺乏有效的方式接触国际买家。传统的寻找供应商方式如阿里巴巴、贸易展会和冷邮件都存在速度慢、成本高和依赖人际关系等问题。中国的小商品出口市场规模庞大，义乌的批发贸易年交易额超过700亿美元。然而，加拿大零售商在寻找竹制厨房用品，或澳大利亚进口商寻找OEM宠物玩具时，缺乏有效的描述需求和匹配合适工厂的方式。为了解决这一问题，作者希望利用大型语言模型（LLMs）来提高匹配效率。
如何为中国小商品出口市场构建一个基于大型语言模型的 B2B 匹配引擎
📄 English Summary
How I built an LLM-powered B2B matching engine for China's small commodity export market
The B2B trade space between Canada and China faces significant challenges, primarily that global buyers struggle to find the right Chinese suppliers while Chinese suppliers lack efficient means to reach international buyers. Traditional methods such as Alibaba, trade shows, and cold emailing are slow, costly, and heavily reliant on personal relationships. The small commodity export market in China is enormous, with Yiwu alone processing over $70 billion in annual wholesale trade. However, Canadian retailers seeking bamboo kitchenware or Australian importers looking for OEM pet toys have no effective way to articulate their needs and connect with the right factories. The author aims to leverage large language models (LLMs) to enhance the efficiency of this matching process.