The Fallacy of Naive RAG: Why Vector Search Fails When Querying Multi-Table Relational Databases

In 2024, the entire AI industry told enterprises to dump their corporate documents into vector databases and perform cosine similarity search. When companies tried using this to ask questions about financial balances or inventory counts, the results were disastrous.

1. Vector Similarity is Not Mathematical Truth

Vector search finds semantic similarity, not relational precision. If you ask 'What was our net margin on product SKU-402 in Q3?', a vector database returns the paragraphs that *talk* about margins, but cannot execute a multi-table SQL JOIN with aggregations and filtering.

$ scarpian-sdc query --mode contextual-relational
[RAW QUERY] 'Reconcile Q3 discrepancies between warehouse receipts and PO ledger'
[NAIVE VECTOR RAG] Hallucinated summary based on generic policy PDFs (INCORRECT)
[SCARPIAN SDC] Generated deterministic AST -> Executed parameterized SQL -> Exact diff: $14,208.12

2. Scarpian Data Core (SDC) Architecture

The Scarpian Data Core sanitizes data in transit, maintaining relational integrity and executing precise parameterized queries rather than guessing statistical word associations.

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