Beginners

• What is Retrieval-Augmented Generation (RAG)? What is any of this?
• We have libraries in Python and TypeScript; the Python lib has more features
• Installation and getting started:
  • Python
  • TypeScript
• I want to customize…
• LlamaHub is a collection of data connectors, agent tools and pre-written code
  • We have notebooks that show how to use the tools on LlamaHub
• create-llama is a command-line tool that sets up a starter full-stack app for you
  • Installation instructions
  • We strongly recommend starting with this if you’re not sure where to start!
• The RAG CLI is a command-line tool that will index and query files for you
  • Good for quickly checking if indexing documents works
• Building a low-code chatbot with Streamlit (this tool is called RAGs)
• What can I use?
  • All the vector stores we support
  • All the LLMs we support
  • All the embedding models we support
• What’s the best way load and parse data?
  • The ingestion pipeline
• How do I get full control over how my query engine works?
  • The query pipeline

Intermediate

• We have several pre-built repos you may want to fork and modify:
  • SEC Insights, an advanced full-stack RAG app using multi-document agents
  • A full-stack demo repo using MongoDB Atlas
    • A (very similar) demo repo using Azure Cosmos DB (a MongoDB clone)
  • Create_llama_projects is a list of starter apps that can be installed with create-llama
    • RAG over embedded tables
    • Multi-document agent
    • Multi-modal (images)
  • Llamabot, a Slack bot
    • Gives step-by-step instructions to build a Slack bot with memory
• LlamaParse, our advanced PDF parsing service
  • There’s a special $500 + swag prize for best use of LlamaParse!
• Customizing prompts
• Hybrid search
  • Vector search + keyword (BM25) search

Advanced

• These are mostly notebooks showing how we did it
• Customizing module types
  • Every component — retrievers, parsers, query engines, agents — can be customized.