I Built Vector-Only Search First. Here's Why I Had to Rewrite It.

I spent three weeks building a pure vector search for an e-commerce product catalog. Embedded everything with multilingual-e5-large , loaded it into Qdrant, and ran my first test queries. "Gift for someone who likes cooking" returned kitchen knives and spice sets. Great. "Nike Air Max 90 black" returned Adidas running shoes. "XJ-4520" (an actual product SKU) returned a random kitchen appliance. I had a semantic search engine that understood meaning but couldn't handle the simplest exact-match lookup. What Vector Search Is Good At Embeddings map text into a high-dimensional space where similar meanings cluster together. When a customer types "gift for someone who likes cooking," the embedding lands near kitchen knives, cookbooks, and spice sets, even though none of those products contain the word "gift." For descriptive queries, it works well. I tested it across five languages and the model ( intfloat/multilingual-e5-large ) mapped them all into the same space. A query in Bulgarian agai