Feb 24
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Building a local AI assistant that can chat with your private documents is one of the most practical ways to use Large Language Models today. This video walks you through the entire process of building a Personal RAG (Retrieval-Augmented Generation) Assistant from scratch using the most popular open-source tools in the ecosystem. In this tutorial, we dive deep into the "why" and "how" of local AI, comparing Streamlit with other UI frameworks and showing you how to orchestrate the entire pipeline with LangChain and Ollama. 🚀 What You’ll Learn - Local LLMs with Ollama: How to run powerful models like Llama 3 or Mistral locally on your machine. - The RAG Pipeline: Understanding Document Loading, Chunking, Embeddings, and Vector Storage. - Orchestration with LangChain: Connecting your local model to your data for context-aware responses. - UI Framework Showdown: Exploring Streamlit - Privacy First: Building a system that works 100% offline with no API keys or data leaving your computer. Github Link: https://github.com/Mayurji/Exploring-UI-Frameworks/tree/main/streamlit-apps/personal_rag_assistant 🛠️ Tech Stack Frontend: Streamlit Orchestration: LangChain Local LLM Engine: Ollama Vector Database: ChromaDB (or FAISS) Language: Python 3.10+ 📂 Resources & Code GitHub Repository: [Link to your Repo] Ollama Download: https://ollama.com/ LangChain Documentation: https://python.langchain.com/ Streamlit Docs: https://docs.streamlit.io/ 🕒 Timestamps 0:00 - Introduction to UI Frameworks 1:00 - Quick start with Streamlit 2:00 - Introduction to Personal RAG Assistant 3:45 - Set up Details - Configs, Packages, and Ollama Models 6:00 - Building RAG Engine 10:00 - Building Streamlit UI 16:45 - Testing the Assistant with PDFs 💡 Prerequisites Python installed on your system. Ollama installed and running. Basic knowledge of Python and Virtual Environments. If you found this helpful, please give it a 👍 and Subscribe for more local AI tutorials! #LocalAI #RAG #Ollama #LangChain #Streamlit #Python #GenerativeAI #AIAssistant