clevilll / bd_hackchallenge

The Holy Grail of Industrial CoPilot

The Holy Grail of Industrial CoPilot

In the heart of the manufacturing world, where machines whisper the secrets of efficiency and innovation, lies a challenge as old as the industry itself – the maintenance, repair, and fault detection of complex machinery.

The Status Quo approach requires to navigating through extensive technical documentation, which encompasses hundreds of pages per machine.

The concept of an Industrial CoPilot, powered by Retrieval Augmented Generation (RAG) technology, emerges as a promising solution to this challenge. RAG can extract the needle of relevant information from the haystack of technical documentation.

However, the integration of complex technical documents into a RAG system poses a significant challenge, primarily due to the multimodal nature of these documents, which include text, graphical data, structural hierarchies and cross-references.

“The challenge is daunting. Thousands of pages of technical documentation, brimming with diagrams, tables, and references, form a labyrinthine puzzle that has stumped many.” - Robert

This is where you step in. Armed with creativity and cutting-edge technology you are invited to explore uncharted territories in search of the Holy Grail of Industrial CoPilot. Attached you find a selected technical Document that will be the basis for this hackathon and a list of hard to solve problems. Feel free to choose any problem you deem interesting or tackle them all.

Food for Thought as Starting Point

The potential approaches are as varied as they are promising:

1. Agent Systems: This approach proposes the development of agent-based systems, such as Autogen, which decompose worker queries into more manageable sub-problems. These sub-problems could then be addressed by specialized agents capable of performing targeted searches within the documentation. For instance, an agent might identify and follow cross-references within documents to facilitate comprehensive information retrieval (see Benchmark Query X)
2. Multimodal Parsing: Enhancing traditional text-based PDF parsing with multimodal parsing capabilities, possibly through the application of technologies like GPT-4 Vision, represents another promising avenue. This method aims to achieve a holistic understanding of technical documents by interpreting both textual and non-textual elements, such as diagrams and tables, which are integral to technical documentation.
3. Smart UI Development: The creation of a smart user interface that efficiently displays relevant sections of the original technical documentation and provides contextual information through a chatbot UI is another innovative solution. This approach focuses on improving the accessibility and navigability of technical documents, facilitating quicker and more effective information retrieval by the workforce.