Fastest RAG pipeline with Llama3.2 & LlamaIndex for FREE #llama3 #lowcode #generativeai #ai #llm

Video Description

Join this channel to get access to perks: @superlazycoder1984 Click "Super Thanks" to support this channel Meta recently released Llama 3.2, which includes small and medium-sized vision LLMs (11B and 90B), and lightweight, text-only models (1B and 3B) that fit onto edge and mobile devices, including pre-trained and instruction-tuned versions. In this tutorial, we walk you through the steps of using the powerful Llama3.2 lightweight text models to create a RAG pipeline. We'll guide you on how to install Groq, set up your environment, ingest dataset in RAG, configure the API key, and work with the Llama3.2 1B and 3B text models. ---------------------------------------- [Key Takeaways] 🔥 Meta has released Llama-3.2, their first open-source multimodal model—an exciting advancement that paves the way for more refined models in the future! 🛠️ Building a RAG System: Learn how to set up a Retrieval-Augmented Generation (RAG) system step by step, starting with the installation of necessary libraries and configurations. 🔑 API Key Setup and Data Ingestion: Discover how to securely set up your API key and ingest the relevant data to prepare for the model's operation. 📚 Data Chunking and Model Definition: Understand the importance of chunking your data for efficient processing and how to define the model to handle those chunks. 🧠 Configuring Context and Vector Index: Learn how to configure contextual information and create a vector index to improve the accuracy and relevance of your queries. 🔍 Query Execution with Llama3.2: Set up a query engine and perform queries using the Llama3.2 1B model, enabling you to retrieve and generate highly contextualized responses. ------------------------------------------- [Links] Google Colab notebook - https://gist.github.com/ruchibahl18/414220e58a60f4d3870c98c2ff3cc059 Llama3.2 Blog - https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/ --------------------------------------------- [Timestamps] 0:00 Introduction 1:11 Lets build RAG 1:26 Installing and Importing libs 3:46 Setting API key 4:23 Data Ingestion 6:00 Chunking 7:08 Defining Model 8:35 Configure Contexts and Vector Index 11:18 Loading Index 12:32 Create query engine 13:09 Query with Llama3.2 1B 13:57 Conclusion 15:21 Like, Share and Subscribe #lowcode #lowcodeplatform #fast #fasttutorial #textmodels #textgeneration #ragpipeline #documents #embedded #vector #nocode #copilot #aider #groq #llama3 #gemini #google #generativeai #aiassistance #coding #codingassistance #codingassistant #github #githubcopilot #coding #generativeai #copilot #llama3 #ai #llm #largelanguagemodels #largelanguagemodel #llms #artificialintelligence #machinelearning #programming #codinglife #programmer #tutorial #education #youtube #youtubetutorial #youtubetutorialforbeginners #youtubetutorials #artificialintelligencetechnology #meta #togetherai #napkins