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prerequisites
Intermediate Linux • Basics of LLMs
skills learned
Create embeddings from a body of text • store embeddings in a vector database • build a RAG LLM application • create a Discord chatbot
Michael Yuan and Tony Yuan
1 week · 4-6 hours per week · BEGINNER
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In order for the Llama-based chatbot app to answer chemistry questions reliably and without hallucination, you need to ground and supplement it with facts and knowledge. That’s why your local school district has tasked you with using RAG (Retrieval Augmented Generation) to help improve the capabilities of the chemistry chatbot app. RAG allows an LLM to search a knowledge base to help answer questions, avoiding unfortunate hallucinations. You’ll create a vector database for chemistry textbooks as the knowledge base, establish an RAG API server for the chatbot, and then deploy your new bot to both the web and Discord.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

prerequisites

This liveProject is for software developers and IT professionals who are interested in building LLM applications in their own domains. To begin this liveProject, you will need to know the following:


TOOLS
  • Intermediate Linux
  • Basics CMAKE and Linux dev tools
  • Basics of Qdrant vector database
  • Basics of Python
TECHNIQUES
  • Command line tools
  • Basics of LLMs
  • Basics of model training and inference
  • Deploying and testing web services

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