One Less Database: Running RAG Search Inside Oracle 23ai
Every RAG tutorial starts the same way: spin up a Pinecone index, generate some embeddings, wire it into LangChain, and call it a day. That's exactly what I did for a customer-support search tool I built last year until I realized I was paying to sync data between two databases that both claimed to be "the source of truth." Oracle 23ai's native VECTOR type looked like a way to collapse that stack into one system, so I spent a week migrating a real workload off Pinecone and into Oracle to see if it actually held up. Here's what I found.
Why bother moving off a dedicated vector database
The pitch for Oracle 23ai's vector support isn't "AI database," it's "one less database." Instead of exporting rows to a separate vector store and keeping the two in sync with webhooks or batch jobs, the embeddings live in the same table...
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