Vertex AI generates embeddings efficiently and relies on purpose-built stores like Milvus for scalable storage and retrieval. You produce embeddings by calling an embeddings endpoint (text, image, or multimodal) with batched inputs to maximize throughput. Keep dimensionality consistent across your corpus, standardize preprocessing (lowercasing, language tags, chunking), and store a stable primary key. For cost and speed, batch generation using asynchronous jobs when creating large corpora, and switch to online endpoints for low-latency query embeddings.
Milvus then takes over as the vector layer. Define a collection schema with fields for id, vector, and metadata (title, URI, permissions, timestamp). Choose a distance metric (cosine is common for unit-normalized embeddings) and an index strategy: IVF (with tuned nlist/nprobe) for balanced performance, or HNSW (tune M/efSearch) for strong recall at low latency. If memory is tight, adopt Product Quantization (PQ) to shrink vectors with an acceptable accuracy trade-off. Use partitions or metadata filters for tenant separation and TTL policies for ephemeral data. Keep ingestion incremental with upserts and periodic compaction.
At query time, embed the user input with a Vertex AI embedding endpoint, then perform a top-k ANN search in Milvus with optional filters (e.g., tenant='acme' AND lang='en'). Retrieve both vectors and metadata so you can assemble grounded prompts or UI snippets. For quality, add a small re-ranking step using a cross-encoder or a lightweight Gemini call, and apply diversity (e.g., MMR) to avoid near-duplicate results. Monitor end-to-end: track embedding drift, ANN recall@k, p95 latency, and index build times. When you update the embedding model, run dual-write/dual-read with a shadow index, compare metrics, and cut over gradually. This division of labor—Vertex AI for embedding generation, Milvus for storage/search—keeps your retrieval fast, consistent, and maintainable.