🚀 Join me as I dive into the world of AI with LLaMA 3! In this video, we'll explore how to create a powerful RAG (retrieval-augmented generation) app using LLaMA 3 to enhance your projects with intelligent data retrieval. 🧠💻
Advanced Chunking Strategies: [ Ссылка ]
🔍 What You Will Learn:
Downloading and Setting Up LLaMA 3: Get started by installing the necessary libraries and downloading the LLaMA 3 model.
Creating the RAG App: Step-by-step process of building the app, from loading data from a URL to saving it in a vector database.
Designing a User Interface: Implement a UI where users can interact by asking questions to retrieve contextually relevant responses.
Enhancing Performance with Nomic Embeddings: Upgrade your app by integrating specialised embedding models for improved accuracy.
🔗 Components:
Ollama to Download LLaMA 3
Vector Databases: Chroma DB
Gradio: An easy way to build custom UIs for your projects
👍 Why Watch This Video?
Gain hands-on experience in AI application development, from basic setups to advanced data handling techniques, all tailored to empower your software development and data science skills.
🔗 Resources:
Sponsor a Video: [ Ссылка ]
Do a Demo of Your Product: [ Ссылка ]
Patreon: [ Ссылка ]
Ko-fi: [ Ссылка ]
Discord: [ Ссылка ]
Twitter / X : [ Ссылка ]
Code: [ Ссылка ]
📌 Timestamps:
0:00 - Introduction to LLaMA 3 and RAG App
0:35 - Setup and Downloads
1:10 - Building the RAG App Core Functionality
3:00 - Embedding Generation and Storage
4:05 - Creating and Integrating the User Interface
5:25 - Final Testing and Demonstration
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