Vertex AI integrates cleanly with Milvus by separating responsibilities: Vertex AI generates and consumes embeddings, while Milvus stores, indexes, and searches them at scale. Your pipeline first calls a Vertex AI embedding model to convert text, images, or metadata into fixed-length vectors (for example, 768–3072 dimensions depending on the model). You then upsert these vectors and associated metadata into Milvus, choose an index type (e.g., IVF, HNSW), set distance metrics (cosine or L2 are common), and build the index. At query time, you embed the user input with Vertex AI again, perform a top-k search in Milvus, and pass the retrieved passages back to your model for grounded responses.
This pattern underpins retrieval-augmented generation, semantic search, recommendations, and memory for agents. Milvus contributes low-latency ANN search, filtering on metadata (e.g., tenant, language, permissions), and horizontal scalability across millions or billions of vectors. Vertex AI contributes robust embedding models, model endpoints for generation, and orchestration via pipelines or serverless functions. You can also implement re-ranking: retrieve ~100 candidates from Milvus, then re-rank with a lightweight cross-encoder or Gemini call to improve precision at the top.
Operationally, keep embeddings and metadata aligned with stable IDs. Store the raw source text, URI, and permissions in Milvus or an adjacent store; use partitions or metadata filters for multi-tenant or time-sliced indexes; and adopt a change-data-capture (CDC) or streaming job for continuous updates. Measure recall/latency trade-offs by tuning Milvus index parameters (e.g., nlist, nprobe for IVF; M and ef for HNSW). For quality control, track embedding drift when models change: re-embed incrementally, run offline evals, and keep side-by-side indexes during migration so you can cut traffic over safely.