LangChain Basics for Beginners

Knowledge Graph-based RAG System Implemented with Neo4j: Next-generation search technology that leverages data relationships beyond simple vector search Maximize RAG performance with the power of graph databases!

This lecture covers RAG (Retrieval-Augmented Generation) system fundamentals and implementation using LangChain. Participants will understand RAG's core principles and learn to build and evaluate systems using LangChain.

Implementing a Retrieval-Augmented Generation (RAG) Intelligent AI Agent with LangGraph! From theory to practice, this is a hands-on tutorial that even beginners can easily follow.

This is a basic Python course for non-major beginners. Theory textbooks are provided through Wikidocs, and practice materials are provided through Github.

This course is designed for beginners and consists of easy explanations and various hands-on projects. It covers data collection using web crawling and LangChain tools, and summarization, extraction, sentiment analysis, and recommendation systems using LLM.

Create your own AI chatbot using Python's basic syntax and libraries. Learn through a step-by-step process of completing 5 projects, including PDF document-based RAG, and deploy them as web services.

This is a course to learn the concepts and usage methods of the LangChain library, and to build your own ChatGPT using the LangChain library.

Envision the future with OpenAI API! OpenAI is a leader in the field of artificial intelligence, always providing cutting-edge technology. The GPT-4-based OpenAI API enables human-like conversations, demonstrating remarkable capabilities applicable to various fields.

Create a program using the GPT API and even generate images and videos!

[Source Code Provided] No coding knowledge required. Build it instantly in your browser using Google's latest tool (IDX) without any installation! Stop studying coding syntax! This is an ultra-fast, hands-on course where you'll build an RAG chatbot that perfectly understands internal company documents through AI conversation (Vibe Coding) in the Google IDX environment and deploy it live to the web.

LLM: A pro quickly delivers core content, from essentials to practical tips!

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!

Develop various features using Spring WebFlux. This course will teach you WebFlux's essential concepts and practical implementation.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

Chatgpt is a program that reads and comments on posts written by my neighbors Lesson on developing marketing automation and neighbor management programs

This is a hands-on tutorial on how to integrate with IntelliJ and develop quickly using the Model Context Protocol (MCP).

This course covers the entire process of designing and implementing generative AI services centered on LangChain 1.0 and LangGraph through step-by-step hands-on practice. Beyond simple LLM calls, you'll directly implement operational AI system architectures that include agent-based architecture, state management, memory, streaming, middleware, and Human-in-the-Loop. Through hands-on practice with document/PDF/web data-based RAG systems, SQL Agent (Chinook DB), tool-calling-based Agents, Supervisor pattern multi-agents, and state machine-based workflows using LangGraph Graph API, you'll build reusable agent pipelines that can be immediately applied in real-world scenarios. Additionally, through structured output (Pydantic-based), agent middleware (Summarization, HITL, Retry, PII protection), and token/step-by-step streaming, you'll complete generative AI applications with the stability, scalability, and controllability required in actual services. 👉 For those who want to accurately understand the internal structure and execution flow of LangChain/LangGraph 👉 For those who want to implement RAG·Agent as a real service structure, not just a "demo" 👉 This is the optimal course for those who need a realistic practical roadmap covering state-based agents, SQL·document automation, and multi-agent orchestration.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is currently the technology that has come closest to AGI. In this lecture, we will explore LLM and Agent technologies from a layperson's perspective.

The goal is to master the complex GPT API and Python in the easiest way possible through hands-on practice. You will develop a YouTube video summarization AI using the latest ChatGPT API and implement it as a web application using Streamlit.

During the Gold Rush, it was said that it was easier to become rich by selling gold mining tools than by mining gold yourself. How about selling programs in the ChatGPT Age of Exploration?

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

Automate YouTube Shorts creation with AI!! Learn how to automatically create YouTube Shorts using n8n from scratch. We will personally build a workflow that handles everything at once: Text → Image/Music → Video Creation → Uploading. Practice includes distinguishing between test/production modes and tips for saving costs. By the end of the lecture, you will complete 3 automation templates that can be used immediately. We will learn step-by-step, starting from node placement, so you can follow along even without coding knowledge. Join us in automating AI YouTube Shorts production!

Learn the easiest way to handle AI with Spring AI. Let's learn Spring AI by building a RAG chatbot.

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.