One Document Type, a Million Files: Structured Extraction into the SQL Table RAG Queries

For a corpus made of one repeated document type (renewal notices, purchase orders, statements), the right approach is to interview the people who handle the documents to derive a small set of columns (6-10 fields), rather than running expensive LLM extraction to discover a schema. Two signals validate a real column: it's named instantly without hesitation, and two separate handlers name it the same way with the same meaning. Fields that fail these tests are subtypes, judgments, or ambiguous terms. The resulting index acts like a table of contents, pointing to candidate documents rather than storing answers, and it grows incrementally as new filterable questions arise. Ingestion cost is paid once per document while querying cost is paid once per question, so the prepared approach pays off once questions outnumber documents — roughly fifty queries a day for a claims handler.

Source: https://towardsdatascience.com/one-document-type-a-million-files-structured-extraction-into-the-sql-table-rag-queries. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.