Rag

Semantic Search with an Open Embeddings API: 990 Tickets, 5 Languages, and Where Keyword Search Still Wins

We indexed 990 support tickets with a 384-dimension open embedding model over an OpenAI-compatible /v1/embeddings endpoint and searched them 1,290 ways, scored against known intents and a TF-IDF keyword baseline. Same-language, keyword search was better. Across languages it collapsed to chance while embeddings held 47 to 58% precision. Adding a 27B reranker over the top ten lifted every language by 17 to 37 points for $0.09 per thousand queries. The whole index cost a tenth of a cent.