Mar 17
Loading video player...
Modern AI applications require more than just powerful language models. Developers need a framework that can connect AI models with memory, tools, APIs, and external data sources to build real-world applications. In this video, we explore LangChain, one of the most widely used frameworks for building scalable AI applications using large language models. You’ll learn what LangChain is, why companies use it, and how it helps developers build AI systems faster and more efficiently. We explain the core components of LangChain, including chains, memory, tools, and LLM integration, and how these components work together to manage user inputs, retrieve context, and generate structured responses. To make the concept practical, we also walk through a simple AI project where a system reads a document and answers questions from it. By the end of this video, you will clearly understand how LangChain works internally and why it is becoming an essential tool for building modern Generative AI applications. 00:00 Introduction – Why normal AI guesses answers without company data 00:28 What is LangChain and why developers use it for AI applications 01:00 Importance of using frameworks when building AI apps 01:32 Smart employee analogy to explain LangChain 02:19 What “context” means in LangChain systems 02:52 LangChain compared with Django and React frameworks 03:29 LangChain workflow explained (User → Chain → Memory → Tools → LLM) 04:40 Real company use cases of LangChain 06:57 Introduction to the Study Buddy AI project 07:36 Project workflow overview (PDF → Embeddings → Vector DB → Answer) 08:12 Chunking, embeddings, and vector database explanation 09:46 Why Google Colab is used instead of local systems 10:48 Installing LangChain and required packages 11:20 Connecting LangChain with OpenAI GPT model 12:30 Setting up OpenAI API key 14:01 Uploading and loading PDF files 18:29 Splitting documents into chunks with overlap 22:46 Creating embeddings and storing them in FAISS vector database 24:31 Configuring ChatOpenAI model (GPT-3.5 Turbo) 26:09 Building the Retrieval QA system 29:11 Asking questions to the AI system 33:24 Example answers generated from the PDF 34:03 Final thoughts and project source code project link:-https://colab.research.google.com/drive/1W_nFgcsULg32zCY1SWSt5mr8P4rdOAdZ?usp=sharing 💡 What is MicroDegree? 🚀 MicroDegree is an online platform teaching programming & job-ready IT skills in Kannada, empowering learners from tier 1 & 2 cities with real career opportunities! 💼 📚 Courses: mdegree.in/ytdscr_courses 📲 Telegram: mdegree.in/md_telegram 📺 Subscribe: mdegree.in/ytdscr_channel 📩 hello@microdegree.work | 📞 0804-710-9999 Follow us on: 🌐 Website: microdegree.work 🔗 LinkedIn: https://mdegree.in/md_linkedIn 📘 Facebook: https://mdegree.in/md_facebook 📸 Instagram: https://mdegree.in/md_instagram 🐦 Twitter: https://mdegree.in/md_twitter