Build Your First RAG With Python (For Beginners)

⚡️ Need help shipping production-ready AI agents so you can scale without hiring? Learn more → https://yomitech.io/utm_source=RGEJhcWdXR4 Apply to work with me: https://www.upwork.com/freelancers/~010b0cdcc4c2129415?mp_source=youtube_bio In this video, we're diving into the world of AI and showing you how to build your first Retrieval Augmented Generation (RAG) system using Python and Langchain. We'll walk you through the process of transforming a PDF document into a powerful chatbot that can answer questions and provide information 24/7, saving you time and effort. What you'll learn: - How to load and process documents like PDFs and text files. - How to build a knowledge base from scratch using Python, Langchain, and Chroma as a vector database. - The process of embedding documents and user queries to create a seamless Q&A system. - How to implement a similarity search to retrieve relevant information. - Tips on deploying your RAG system to the cloud for production use. Don't forget to subscribe and hit the bell icon to stay updated on our upcoming series, where we'll explore deploying your knowledge base on the cloud and embedding it on a website. Chapters: 0:00 Introduction and Overview 1:13 What is Retrieval Augmented Generation (RAG)? 1:46 How to build a RAG/knowledge base 4:43 Demo 5:12 Building the Knowledge Base 12:14 Querying the Knowledge Base 14:27 Deployment tip 🔗 Resources: - GitHub repository: https://github.com/JonathanMiz/first-rag-tutorial - OpenAI Functions Video: https://youtu.be/78x1dB4poaE - OpenAI Chatbot for Telegram Video: https://youtu.be/dNIQgDVDt1k Thank you for watching, and we'll see you in the next video! #retrievalaugmentedgeneration #aiagent #langchain