RAG - Talk with your Files
- Andreas Mueller
What is RAG?
In the context of RAG, vector databases play a crucial role in the retrieval stage: Efficient information retrieval: When a user poses a query, the RAG system leverages the vector database to efficiently search through a vast corpus of text or documents.
In short, we encourage you to upload your documents (in PDF or text format) and ask questions about them. For example, if you have uploaded a homework assignment and need help with question 3 that your teacher gave you, AI can assist you. As a busy manager, you may not have the time to completely read through all of the documents you receive, but the AI can provide you with a summary.
Regarding the uploading of documents and asking questions, we would like to stress that our AI system is designed to help you in any way possible. Whether you need assistance with solving math problems, or understanding complex legal agreements, our AI can provide you with the answers you need quickly and efficiently.
Furthermore, as a busy manager, you may not have the time to thoroughly read through all of the documents you receive. Our AI system can help by providing you with a summary of the most important information so that you can make informed decisions without wasting your valuable time reading through every detail.
With Gnoppix 25.5, you can now assign your own trained RAG model to the RAG function. This eliminates dependence on the base model.