How to Use LLaMA 3 8B with RAG in Python | Best Setup for Retrieval-Augmented Generation (2025)

In this tutorial, learn how to integrate LLaMA 3 8B with Retrieval-Augmented Generation (RAG) in Python for building smart, context-aware applications. We’ll guide you through loading the model, connecting it with a retriever (like FAISS or ChromaDB), and generating accurate answers using real documents. This setup is one of the best local RAG pipelines you can build in 2025 using LLaMA 3 8B.
What You'll Learn:

Setting up LLaMA 3 8B for local use
Implementing RAG architecture with Python
Using ChromaDB or FAISS as a vector store
Building a complete question-answering system with context