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Retrieval Augmented Generation with Llama 2

Python Prerequisites and Llama 2 Access

Retrieval Augmented Generation 101

Creating Embeddings with Open Source

Building Pinecone Vector DB

Creating Embedding Dataset

Initializing Llama 2

Creating the RAG RetrievalQA Component

Comparing Llama 2 vs RAG Llama 2

Description:

Learn how to enhance Llama 2 using Retrieval Augmented Generation (RAG) in this informative tutorial video. Discover how RAG keeps Large Language Models up-to-date, reduces hallucinations, and enables source citation. Follow along as the instructor builds a RAG pipeline using Pinecone vector database, Llama 2 13B chat model, and Hugging Face and LangChain code. Explore topics such as Python prerequisites, Llama 2 access, RAG fundamentals, creating embeddings with open-source tools, building a Pinecone vector database, initializing Llama 2, and comparing standard Llama 2 with RAG-enhanced Llama 2. Gain practical insights into implementing RAG for improved AI performance and accuracy.

Better Llama with Retrieval Augmented Generation - RAG

James Briggs

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#Computer Science #Artificial Intelligence #Natural Language Processing (NLP) #LLM (Large Language Model) #LLaMA (Large Language Model Meta AI) #Programming #Programming Languages #Python #LangChain #Machine Learning #Hugging Face #Retrieval Augmented Generation (RAG)

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