Building a small local knowledge base on top of Actian VectorAI DB, the actual challenge wasn't the database. It was easy to accidentally fake semantic search, and just as easy to reach for automation tools, or an LLM, in the wrong place. Here's the split that fixed it, plus the grounded answer step I added on top of search. The full build is on GitHub.
Two ways to fake it
The first pass at this stored a single note and searched for that same note. It "worked," in the sense that the top result was always right. That doesn't prove anything about semantic search: with one point in the database, of course it wins.
The other early version chained four n8n HTTP Request nodes straight into the database. That's not using n8n for anything; it's a curl command dressed up as a workflow.
Both come from the same mistake: not being clear on what each tool is actually for.
