Build a RAG solution with your data & Azure OpenAI скачать в хорошем качестве

Build a RAG solution with your data & Azure OpenAI

This Video helps in giving deep understanding of RAG (Rertrieval Augmented Generation), its real time use case, Tools and Technique required to understand RAG,its deployment on OpenAI Azure and access uploading data to Blob storage using API with example. Detailed Description of Video Implementation of Retrieval-Augmented Generation (RAG) on Azure OpenAI to enhance AI response accuracy through document retrieval and model deployment. This tutorial covers Retrieval-Augmented Generation (RAG) and its implementation on Azure OpenAI. RAG combines retrieval mechanisms with generative language models for improved AI response accuracy. Users can upload data to Azure and deploy AI models for enhanced document retrieval. The setup involves creating resource groups, storage accounts, and deploying models like GPT-3.5 Turbo. Successful deployment allows users to query documents and receive relevant responses through an API. Download traveldata using below URL (data used in Video tutorial) https://aka.ms/own-data-brochures Github link to access environment file and access python code: https://github.com/aakkat/How-to-Buil... Chapters: 01:32 Understanding RAG Methodology 02:07 Integrating Azure OpenAI 07:03 Setting Up Azure Resources 08:15 Configuring Azure Resources 15:46 Copying Keys and Endpoints 22:28 Uploading Data to Blob Storage 23:28 Deploying AI Models 34:26 Running Python Application