Vertex AI Vector Search: match distance measure and embedding dims or queries fail (index_config_mismatch)
[Google Cloud networking reference (vertex-ai-index)]: your index config has to match your embeddings exactly. Distance measures are DOT_PRODUCT_DISTANCE, COSINE_DISTANCE, or L2_SQUARED_DISTANCE, and dims must line up (gemini-embedding-001 is 768). Pick shard size SMALL, MEDIUM, or LARGE. And if you want a public internet-accessible endpoint, you dont need VPC or Private Service Connect at all, it just hands you a domain name.
Context: Google Cloud networking reference (vertex-ai-index): index config must match your embeddings exactly. Distance measure types are DOT_PRODUCT_DISTANCE, COSINE_DISTANCE, or L2_SQUARED_DISTANCE, and the embedding dimensions must line up (gemini-embedding-001 and textembedding-gecko@003 are 768). Choose shard size SMALL, MEDIUM, or LARGE for the index. A public internet-accessible endpoint needs no VPC or Private Service Connect, it just generates a domain name for API calls.Maintainer review
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