A local visual RAG challenge for huge construction PDFs
A local system for construction documents needs more than basic chat with a PDF. The first real test file is an 828-page PDF of about 1 GB, with contract text, , schedules, complex tables, and construction drawings mixed together.
Some pages use a large drawing format around 36 by 48 inches, with text near diagrams, callouts, detail tags, and trade-specific sheets where layout matters. The goal is to keep table structure, extract contract language, understand drawing context at a basic level, and answer plain-language questions with .
Accuracy matters more than speed. The hard choices are the , the , which maintained tools to rely on, and which parts should be built directly with ChatGPT, Codex, or Claude instead of premade tools.
Key points
- The test document is an 828-page construction PDF of about 1 GB.
- The file combines contract text, , schedules, tables, and drawings.
- Large-format drawing pages include diagrams, callouts, detail tags, and trade-specific sheets.
- The target system should preserve tables, extract contract language, read basic drawing context, and cite pages.
- Accuracy is more important than speed, and tool-vs-custom-build choices are a central concern.