RAG Series (10): Hybrid Search — Retrieving More, Missing Less

A Blind Spot in Vector Search Suppose your knowledge base contains a document with this sentence: "For Chinese scenarios, we recommend BAAI/bge-large-zh-v1.5 , with a vector dimension of 1024." A user asks: "What is the vector dimension of BAAI/bge-large-zh-v1.5?" You might think this is a gimme — identical words, vector search should nail it easily. Not necessarily. Vector search relies on semantic similarity. When the query and document share the same exact vocabulary, vector search has no par