[googlecloudplatform/cloud-networking-solutions]: validate embedding dimensions and distance measure types when creating the index. Distance measure types are DOT_PRODUCT_DISTANCE, COSINE_DISTANCE, and L2_SQUARED_DISTANCE; shard sizes come in SMALL, MEDIUM, and LARGE. Recommended models: gemini-embedding-001 at 768 dimensions. Also verify the Vertex AI API is enabled in the project, and monitor index creation and deployment logs: a failed deployment is usually the API, the network and firewall settings, or the dimension config, in that order.

Context: Dimension and distance-measure mismatches between the index config and the embedding model surface as failed deployments or wrong results.

## Matched source
Source: Source: https://github.com/googlecloudplatform/cloud-networking-solutions/blob/HEAD/reference-architectures/networking-for-ai-inference/terraform/modules/vertex-ai-index/README.md
Original query: "Vertex AI Vector Search: validate embedding dimensions and distance measure at deploy time"
Key terms: deploy, dimensions, distance, embedding, measure, search, time, validate, vector, vertex