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RAG (Retrieval-Augmented Generation) for industrial applications

For the manufacture, design, and operation of complex machinery, or in the aerospace or automotive sectors, a lot of knowledge is required. Having this knowledge readily available at the right time and place is not simple at all. AI-based assistant systems can simplify the accessibility of complex documentation and thus reduce the need for direct contact with the respective experts. Retrieval Augmented Generation (RAG) systems for enhancing chat-based assistants like ChatGPT with secure access to internal data have become the standard in this area.

Technology

RAG (Retrieval-Augmented Generation)

Retrieval-Augmented Generation (RAG) adds a retrieval step to the workflow of an LLM, enabling it to query relevant data from additional sources like private documents when responding to questions and queries.

Easy and inexpensive: This workflow does not require costly training or fine-tuning of LLMs on the additional documents.

Access Diverse Document Types: RAG can seamlessly access and retrieve information from a wide range of document types, from PDFs and Word files to other proprietary formats. Access diffent soruce from pdf, word to other documents.

Dynamic Document Exploration: The RAG approach enhances the ability to not only retrieve but also explore and analyze documents. This includes clustering, similarity analysis, and consistency checks, which help uncover insights and relationships within indexed documents as well as in response to specific user queries.

Integration with Talk to Data: RAG works in tandem with 'Talk to Data' to enhance data retrieval capabilities, particularly useful for accessing measurement results and simulation outcomes.

Knowledge management for complex machines

Answer any question on your documents

With retrival augmented generation (RAG), you can quickly build assistants that can answer questions on your documents. With our experience, we can help you to create assistants that your team and your customers can trust.

Blog: Learn more about RAG Try our RAG Demo on GitHub