In this video, we dive into implementing an agentic RAG system using Claude 3.5 Sonnet by Anthropic, LlamaIndex, and MongoDB. I'll take you through the concepts of agentic RAG, which combines retrieval-augmented generation (RAG) and agentic systems to create a dynamic system capable of retrieving information efficiently and making autonomous decisions.
We'll cover everything from data loading and embedding to integrating with MongoDB and setting up the agentic system. By the end, you'll know how to build a recommendation system for Airbnb listings and extend it with additional tools. Follow along with the provided notebook and learn practical applications of building advanced AI agents.
⏲︎Timestamp
00:00 Introduction to Agentic RAG System Implementation
01:20 What is Agentic RAG?
02:40 Overview of Agentic RAG and its Components
06:28 More Coding Resources
07:16 Use case for Agentic Systems
07:55 Installing libraries and Environment Variables
08:30 Setting the LLM and Embedding Model
09:40 Data Loading from Hugging Face
11:19 Data Preprocessing
13:46 Embedding Generation
14:10 MongoDB Database and Collection Setup
17:00 Data Ingestion
18:10 Initializing the Query Engine Tool
19:40 Creating the AI Agent
20:25 Starting Conversations with the Agent
24:00 Conclusion and Final Thoughts
🔗 Links
Notebook: [ Ссылка ]
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