Will Gemini Embedding 2 Kill Multi-Vector Search in Vector Databases?

This post analyzes the impact of Google's Gemini Embedding 2 on the necessity and application of multi-vector search in vector databases like Milvus. It differentiates between unified multimodal embeddings and multi-vector retrieval, explaining that Gemini simplifies pipelines for data where all modalities describe the same content. However, it argues that Milvus's multi-vector search remains essential for scenarios involving fundamentally different semantic spaces (biometrics, structured attributes) or combining diverse retrieval goals (keyword precision with semantic recall). The post also suggests a hybrid approach where Milvus collections can house both unified Gemini embeddings and additional specialized vector columns.