RAG

• Packages
  • {ragnar} - Helps implement Retrieval-Augmented Generation (RAG) workflows.
    • Focuses on providing a complete solution with sensible defaults, while still giving the knowledgeable user precise control over all the step
    • Designed to be transparent, so you can inspect outputs at intermediate steps
• Resources
  • Rapid RAG Prototyping - Example using {ellmer} and DuckDB’s vss extension (See Databases, DuckDB >> Extensions >> Vector DBs
• Process (source)
  
• Pros
  • Context Awareness: Improves accuracy
  • Source Citation: Access to sources enhances the transparency of the responses
  • Reduces Hallucinations
  • Good for domain-specific data
• Cons
  • Latency: Response times can be problematic for real-time applications
  • An external knowledge base is required
• Use Cases: Document Querying, Coversational Agents, Personalized Recommendation, Content Generation
• Versus Fine-Tuning (source)