Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Grab it
Build a simple Retrieval-Augmented Generation (RAG) system from scratch using Llama 3.1, Groq API, Sqlite-vec, and FastEmbed in this comprehensive tutorial video. Learn to create a chatbot with custom data without relying on external libraries like LangChain and LlamaIndex. Explore the process of setting up Google Colab, utilizing sqlite-vec for vector storage, adding custom data to the database, creating document embeddings, and understanding how vectors are stored. Discover techniques for similar document search and constructing essential RAG components. Practice interacting with the chatbot using your custom data and gain insights into building efficient AI-powered information retrieval systems.
RAG from Scratch with Llama 3.1 - Building a Custom Data Chatbot
Add to list
#Computer Science
#Machine Learning
#Retrieval Augmented Generation (RAG)
#Artificial Intelligence
#Chatbot
#Natural Language Processing (NLP)
#LLM (Large Language Model)
#LLaMA (Large Language Model Meta AI)
#Programming
#Databases
#Vector Databases
#Data Science
#Information Retrieval
#Embeddings
0:00 / 0:00