What is RAG?

RAG (Retrieval-Augmented Generation) is a powerful AI technique that enhances language models by combining them with external knowledge sources. Instead of relying solely on the model's training data, RAG allows AI to access and reference specific documents, files, and information that you provide.

How RAG Works

When you ask a question, RAG follows these steps:

  1. Document Processing: Your uploaded documents are broken down into smaller chunks and converted into mathematical representations called embeddings
  2. Similarity Search: When you ask a question, RAG finds the most relevant chunks from your documents
  3. Context Enhancement: The relevant information is added to your question as context
  4. Informed Response: The AI model generates an answer using both its training knowledge and your specific documents

Benefits of RAG in Ollamac

  • Accurate Information: Get answers based on your specific documents rather than general training data
  • Source Attribution: Know exactly which documents informed the AI's response
  • Privacy: Your documents stay local on your machine - no data sent to external servers
  • Up-to-date Knowledge: Include recent documents that weren't part of the AI model's training
  • Personalized Responses: Tailor AI responses to your specific use case, industry, or domain