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Vector Database Schema Design for AI Memory Layers
Most teams treat vector databases as flat blobs — pick an embedding model, dump everything in, and hope semantic search works. It doesn't. This episode unpacks how to deliberately shape your vector data architecture for serious AI memory layers. We cover when to use separate indexes versus namespaces, how to design per-document-type metadata schemas, why hybrid retrieval needs structured filtering before semantic search, and how a query router determines which fields to filter on. If your recall at top-K is a coin flip, the embedding model isn't the problem — your data architecture is…
The skinny
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