Complete Guide to Build RAG App using Ollama Python Lib | Local LLM RAG

Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to creating a RAG LLM App using the Python library "Ollama". It is a wrapper around the "Ollama" tool that lets us access Open-Source LLM on the local machine for free. To build our LLM app, we have used open-source LLM "llama-2" with 7B parameters. For storing & searching embeddings of external docs, we have used the FAISS library. The tutorial is a good starting point for someone who wants to learn how to create RAG LLM Apps. It's a good introduction to the RAG workflow as well. You can easily extend the app to use any other LLMs like Mistral, falcon, vicuna, Gemma, etc.

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CODE - https://github.com/sunny2309/ollama_rag
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Learn Ollama -    • Ollama: Run LLMs on your Local Machin...  
Learn Ollama Python Library -    • Ollama Python Library: Use LLMs on yo...  
RAG App using LangChain -    • Step-by-Step Guide to Build RAG App u...  
RAG App using LlamaIndex -    • Step-by-Step Guide to Build RAG App u...  
What is RAG -    • Let's Talk about AI Buzzword: RAG |  ...  
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Important Chapters:

0:00 - Build RAG LLM App using Ollama Python Library
1:14 - RAG App Workflow
2:14 - Install Ollama
2:44 - Download "llama-2 (TB)" Model
3:08 - Bring up Ollama Server
4:00 - Code Explanation
6:02 - Load External Docs Data
7:32 - Generate Embeddings
8:26 - Create Vector Store Index
10:12 - Create a Retriever to Retrieve Relevant Docs
13:10 - Complete Ollama Retrieval Chain (RAG Pipeline)

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