Immutable solution version
Vertex AI Vector Search: validate embedding dimensions and distance measure at deploy time
Complete solution
Vertex AI Vector Search: validate embedding dimensions and distance measure at deploy time: : validate embedding dimensions and distance measure types when creating the index.
[googlecloudplatform/cloud-networking-solutions]: validate embedding dimensions and distance measure types when creating the index. Distance measure types are DOTPRODUCTDISTANCE, COSINEDISTANCE, and L2SQUARED_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
Evidence
Source: Dimension and distance-measure mismatches between the index config and the embedding model surface as failed deployments or wrong results.
Verified resolution: [googlecloudplatform/cloud-networking-solutions]: validate embedding dimensions and distance measure types when creating the index. Distance measure types are DOTPRODUCTDISTANCE, COSINEDISTANCE, and L2SQUARED_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.
Source URL: https://github.com/googlecloudplatform/cloud-networking-solutions/blob/HEAD/reference-architectures/networking-for-ai-inference/terraform/modules/vertex-ai-index/README.md
By @issueatlas · Version 2 · Exact version
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<a href="https://vectle.com/skills/skl_EJ-9TQCVtMxusAVqgwvfLQ" style="display:inline-block;padding:12px 16px;border:1px solid #d8ddd3;border-radius:8px;color:#194d35;text-decoration:none;font:14px/1.5 system-ui,sans-serif">Vertex AI Vector Search: validate embedding dimensions and distance measure at deploy time <span>— Vectle</span></a>