GenAI RAG Platform for Laboratory Documentation
GenAI Applications • Research Facilities • Technical Documentation
Overview
A Generative AI–based retrieval platform designed to make complex laboratory equipment documentation searchable and operationally useful. The system applies retrieval-augmented generation techniques to large, heterogeneous PDF manuals, enabling natural-language access to critical technical information without modifying the original source documents.
Problem
Laboratory equipment manuals are typically distributed as large, static PDF files that are difficult to search and interpret in practice. Critical operational details are often distributed across narrative text, tables, figures, and scanned images, making it time-consuming for users and staff to locate accurate information—particularly under time pressure in shared research environments.
The goal of this project was to evaluate whether a retrieval-augmented Generative AI system could improve accessibility, accuracy, and usability of complex technical documentation in real operational contexts.
Technical Approach
A retrieval-augmented generation (RAG) system was implemented to support natural-language querying over PDF equipment manuals.
The platform ingests documentation through structured extraction pipelines that preserve both narrative text and tabular content. Documents are chunked, embedded, and stored in a vector database to support semantic retrieval. At query time, user questions are embedded and matched against the document corpus, with the most relevant content provided as grounded context to a large language model for response generation.
An interactive web interface provides a chat-based experience, allowing users to ask operational questions and receive answers grounded in specific sections of the source documentation.
Outcomes
The project demonstrated that a RAG-based approach can significantly improve access to complex technical documentation without requiring changes to existing manuals. The system successfully answered operational questions that typically require manual searching across multiple sections of a PDF, validating the approach for scientific and engineering environments.
The prototype implementation is available as open-source software: GitHub – RAG-bot
Evolution & Next Steps
Subsequent iterations have expanded beyond text-only processing toward multimodal document understanding, using vision-capable language models to interpret scanned manuals, figures, and embedded tables that are poorly captured by traditional text extraction.
In parallel, the vector storage layer has been migrated from a standalone vector database to PostgreSQL with pgvector, enabling tighter integration with relational metadata, improved performance, and more efficient system management. These changes reflect lessons learned from deploying AI systems in institutional environments and inform future production-oriented implementations.