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In this comprehensive video tutorial, we’ll dive into building a private Retrieval-Augmented Generation (RAG) system using LangChain, Ollama, and FAISS Vector Store with Llama 3.2. You’ll learn everything from basics to advanced techniques to create a secure, local solution where your data never leaves your computer.
This guide will cover:
- Understanding the basics of RAG and its applications.
- Setting up LangChain with Ollama and FAISS.
- Using FAISS Vector Store to manage datasets effectively.
- Incorporating Llama 3.2 for advanced question answering.
- A practical implementation of health supplements data for your project.
- Optimizing your RAG pipeline for local execution and privacy.
By the end of this tutorial, you will have built your own private, secure RAG system for question answering without compromising your data privacy. Perfect for anyone working with sensitive data who wants to leverage the power of modern AI models.
Deployment of Machine Learning Models in Production in Python:
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Previous Video:
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Code Files:
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Streamlit Tutorials:
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