From Zero to Hero: Mastering RAG and Embedding Models with MongoDB, OpenAI, and Voyage AI

Video Description

Ready to go from beginner to expert in AI application development? In this comprehensive, hands-on tutorial, Richmond Alake takes you step-by-step through building modern AI-powered applications using Python. Whether you’re a developer new to AI or looking to deepen your understanding of Retrieval-Augmented Generation (RAG) and AI agents, this video is for you! What you’ll learn: - The fundamentals of RAG (Retrieval-Augmented Generation) and how it powers next-gen AI apps - How to collect, process, and embed data using Hugging Face datasets and Voyage AI embedding models - Setting up and using MongoDB Atlas as both a vector and operational database for AI workloads - Implementing efficient search: text search, vector search, and hybrid search in MongoDB - Building a full RAG pipeline and integrating it with OpenAI’s Python SDK - Creating intelligent AI agents and multi-agent systems using the OpenAI Agent SDK - Adding persistent and session-based memory to your agents for real conversational context - Project Use Case: You’ll build a virtual primary care assistant—a healthcare chatbot that can answer questions, reference past conversations, and provide medication guidance, all backed by real data and advanced retrieval techniques. Full code notebook: https://bit.ly/zerotoherowithrag 00:00 - Introduction & Notebook Structure 03:34 - Part 1: Project Use Case & Data Sources 09:04 - Libraries and Frameworks 11:14 - Data Loading from Hugging Face 14:38 - Embedding Generation with Voyage AI 20:55 - Setting Up MongoDB & Collections 26:00 - Creating Indexes for Vector & Text Search 35:35 - Data Ingestion & Exploring the Database 40:13 - Retrieval Methods: Text, Vector, and Hybrid Search 41:25 - Text Search with MongoDB 46:57 - Vector Search with MongoDB 53:54 - Hybrid Search with MongoDB 57:06 - Part 2: Building the RAG Pipeline 57:46 - Setting up and Testing the LLM from OpenAI 59:29 - Augmentation & LLM Integration 1:02:06 - Example RAG Pipeline in Action 1:03:15 - Part 3: Building AI Agents with OpenAI SDK 1:04:37 - Building a Minimal Agent 1:06:32 - Tool-Enabled Agents 1:14:07 - Multi-Tool & Multi-Agent Systems 1:15:43 - Orchestrator & Synthesizer Agents 1:17:05 - Live Agent Demo & Orchestration 1:18:51 - Part 4: Adding Persistent Memory with MongoDB 1:20:35 - Storing & Retrieving Conversation History 1:22:50 - Interactive Chat Session Example 1:26:32 - Part 5: Session-Based Memory with OpenAI SDK 1:29:52 - Custom Session Memory Implementation 1:32:32 - Memory Management & Forgetting 1:34:40 - Final Thoughts & Recap #AI #Python #RAG #MongoDB #OpenAI #Agents #LLM #Tutorial #ZeroToHero Check out the following playlist: 1️⃣ AI Stack Engineer [Learn Series]: https://bit.ly/4f2p3xg 2️⃣ AI Stack Engineer [Startup Series]: https://bit.ly/4bxnO6d LinkedIn: https://www.linkedin.com/in/richmondalake/ Twitter: https://twitter.com/richmondalake