LangChain (OpenAI) Vector Embeddings For Beginners скачать в хорошем качестве

LangChain (OpenAI) Vector Embeddings For Beginners

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-aut... Grabbing vector embeddings is a critical step in building out vector databases. Which is needed when you eventually build out more complex Large Language Models. 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: ryannolandata@gmail.com 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord:   / discord   📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT OpenAI/Langchain Playlist:    • How to Build Your First AI LLM Prompts wit...   FAISS Vector Library:    • FAISS Vector Library with LangChain and Op...   Langchain Chains:    • LangChain Chains for Beginners: An Easy In...   Streamlit Langchain:    • Learn to Build Exciting LLM applications w...   In this tutorial, I show you how to create OpenAI embeddings using Python and LangChain in just a few lines of code. Embeddings are the foundation of modern AI systems, converting text into vector representations that enable powerful capabilities like semantic search, clustering, recommendations, anomaly detection, and classification. I walk you through everything from basic embedding queries to more advanced document embedding workflows. You'll learn how to use different OpenAI embedding models like text-embedding-3-small and text-embedding-3-large, understand what those floating-point numbers actually mean, and see why vector databases depend on these embeddings. We start simple with single text queries, then move into document loaders and text splitters to handle larger content. I demonstrate how to chunk documents using RecursiveCharacterTextSplitter and create embeddings for each chunk, which is essential when building RAG (Retrieval Augmented Generation) systems. By the end, you'll be able to embed entire documents and prepare them for vector database storage. This beginner-friendly guide includes real code examples in Google Colab, clear explanations of each step, and practical use cases. Whether you're building a chatbot, search engine, or recommendation system, understanding embeddings is crucial for any AI application working with text data. TIMESTAMPS 00:00 Introduction to Embeddings 01:02 Setting Up Python Environment 02:07 Importing OpenAI Embeddings 02:56 Embedding Models Overview 03:54 Creating an Embedded Query 05:25 Embedding Documents 06:44 Working with Multiple Documents 07:57 Integrating Document Loaders 09:22 Using Text Splitters 10:40 Recursive Character Text Splitter 11:45 Creating Chunks from Documents 12:52 Embedding Individual Chunks 13:28 Embedding All Chunks with For Loop OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / matt-payne-ceo   Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.