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RAG Manuals Tech Support

Retrieval-Augmented Generation Over Equipment Documentation

Built and delivered

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UNIS Tech Support

RAG over 150+ game manuals โ€” ask anything about UNIS arcade equipment

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The Problem

Tech support staff spent significant time digging through arcade equipment manuals for every ticket. Generic PDF search finds keywords but can't answer questions. Manuals vary wildly in format โ€” tables, flowcharts, buried paragraphs.

The Solution

A custom RAG web app over the full library of equipment manuals. User picks the specific game, asks a natural-language question, and gets three coordinated outputs: a synthesized answer, a PNG snapshot of the actual manual page, and a link to the public manufacturer manual.

Key Features

  • Per-game metadata-scoped retrieval (collapses search to one machine's docs)
  • Layout-aware PDF ingestion (preserves tables, diagrams, multi-column)
  • Visual-proof responses: answer + page PNG + public manual link
  • Source-grounded answers techs can verify with one click

Tech Stack

PythonOpenAI GPT-4ChromaDBPDF layout extractionCustom web chat frontend

Outcome

Technically sound system with the trust-building visual-proof pattern. Key lesson: the page PNG snapshot is what makes it trustable by field techs โ€” they don't trust answers they can't verify.