Mastering RAG: Local Intelligent Apps with Langchain & Ollama

🎉 Welcome to our journey into the world of Retrieval Augmented Generation (RAG)! 🚀

🔍 In this video, we explore how to build your very own local LLM RAG application using Ollama and the open-source model Llama3. Whether you're a developer looking to leverage AI for business or a tech enthusiast keen on understanding cutting-edge AI frameworks, this guide is for you.

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Key Topics Covered:

- Understanding LLM Shortcomings: Discover the common pitfalls of Large Language Models (LLMs) and why they might fail to provide accurate responses for specific and current queries.
- Solutions with Fine Tuning & RAG: Learn about fine-tuning and Retrieval Augmented Generation (RAG) and how these methods can enhance LLM capabilities for practical use cases.
- Navigating Proprietary Data Challenges: Find out how open-source models can help you utilize LLMs while adhering to corporate security policies.
- Building a RAG App: Step-by-step tutorial on creating a RAG application using Langchain, ChromaDB, Ollama, and Streamlit.

🔧 Components Used:

- Langchain: Framework for building LLM-powered applications.
- ChromaDB: Lightweight vector database for storing text data in vector format.
- Ollama: Tool for running open-source models locally with ease.
- Streamlit: Framework for creating interactive web interfaces with Python.