Preparing Quattro Pro Spreadsheet Archives for AI and RAG Search

TL;DR

LLM and RAG pipelines cannot index .wq1, .wq2, .wb2, or .qpw files directly. Convert legacy spreadsheets locally to CSV, Markdown, and XLSX before they enter the pipeline. CSV and Markdown are the best inputs for embedding and retrieval; XLSX and PDF support human review of what the model is being shown.

AI cannot use what it cannot read

Most retrieval and embedding pipelines silently skip legacy spreadsheet binaries. The result is a private RAG system that confidently answers questions from a fraction of the company's actual knowledge—often without anyone realizing what was excluded.

Modernizing the archive is the unglamorous-but-necessary precondition: clean, open formats unlock the spreadsheet history that legacy formats kept dark.

Best target formats for RAG

1. CSV for tabular embedding

CSV is the cleanest tabular input. Each row becomes a small, predictable chunk; columns become natural metadata. Pair CSV with row-level metadata in the index for filtering by year, entity, or source workbook.

2. Markdown for narrative tabs

Many Quattro Pro notebooks include narrative tabs—assumptions, change logs, executive summaries. Markdown is friendlier to LLM tokenization than HTML or PDF text, so summary tabs make better citations after conversion.

3. XLSX and PDF for human review

When a reviewer needs to see what the model was shown, XLSX and PDF are the formats they expect. Keep them next to the CSV/Markdown so model citations can be traced back visually.

Keep the conversion private

If the archive includes finance, customer, employee, or operational data, conversion should happen before any cloud AI step—and preferably inside the same controlled environment that hosts the rest of the RAG pipeline.

Free online converters are the wrong dependency for a privacy-respecting AI system. They reintroduce exactly the data-residency questions a private LLM is designed to remove.

Indexing tips that pay off later

Capture per-file metadata during conversion: source path, output path, original extension, conversion timestamp, and any project, year, or entity that can be inferred from the folder structure. Push that metadata into the vector store as filterable fields.

Sample-test retrieval against a few known historical questions before declaring the archive RAG-ready. The right question often reveals tabs or assumptions that need to come into the index.

Stop building RAG on a partial archive

A private LLM is only as smart as the corpus it can see. Convert the legacy spreadsheet archive once, index it with metadata, and let the model finally read what the company actually knows.