The slides you just saw:

https://slides.com/seldo/genai-agents-tidb

Got live questions? We’re on Discord!

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, vector stores, LLMs, agent tools, pre-written code and more!
• 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 LLMs we support
  • All the vector stores 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
• LlamaParse is our free-to-use industry-leading document parsing service
  • If you’re dealing with complex PDFs or other documents we recommend using it!

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
  • 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
    • There is also a version for Discord contributed by the community