graphRAG - Neo4J로 구현하는 지식 그래프 기반 RAG 시스템 (feat. LangChain)

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

This course covers the basic concepts and implementation of RAG (Retrieval-Augmented Generation) systems using LangChain. Participants will learn to understand the core principles of RAG and how to build and evaluate the system in practice using LangChain.

Use Python's basic grammar and libraries to create your own AI chatbot. Learn how to implement five projects, including RAG based on PDF documents, and deploy them as web services.

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.

Learn the basic concepts and usage of LangChain using Python. The practice will be conducted mainly in the Google Colab environment, and the practice materials will be provided through Github.

This is a basic Python course for non-major beginners. Practice materials are provided through Github.

This course covers the basic concepts and implementation of RAG (Retrieval-Augmented Generation) systems using LangChain. Participants will learn to understand the core principles of RAG and how to build and evaluate the system in practice using LangChain.

I'm learning how to build practical AI agents using LangGraph while working on various AI agent implementation projects using LangGraph.

By implementing various practical data analysis cases using SQL, you can simultaneously improve your data analysis and SQL utilization skills.

Master advanced SQL, solve 101 practical data analysis problems at a professional level together, and build a solid SQL data analysis skillset.

From PRD to AI-powered product recommendations, complete a real-world Next.js shopping mall project with v0, Cursor AI, and various LLMs!

Easy and fun SQL stories learned from professional instructors! Lectures are conducted systematically and step-by-step for beginners. We offer opportunities to learn with sufficient repetition through a 3-step learning process.

Based on Oracle 21C, from concepts to practical application, the easiest way to learn SQL for the first time! Direct lecture by the author of "Do it! Introduction to SQL with Oracle" from Eegees Publishing! Tailored for non-majors and beginners, explaining everything step by step.

If you work with data, SQL is a fundamental skill you must know. In this course, we will learn SQL from a data analysis perspective, focusing on hands-on practice. The practice will be conducted in Google Colab using DuckDB.

Through detailed lectures and hands-on practice on the core elements of SQL, we will provide you with a solid foundation to grow into a SQL analysis expert.

A current professional quickly delivers core content, from foundational knowledge needed for LLM to practical tips useful in the field.

This course will teach you how to create images using DALL-E. Through basic functions, a three-step prompt strategy, and hands-on image creation exercises, participants will become creative image makers.

We examine methods for collecting and analyzing user requirements in order to plan LLM services.

This is a video of the presentation session of the Natural Language Processing Conference <Langcon 2024>

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

By combining CrewAI, which enables LLM-based multi-agent technology, a knowledge graph, and an inference system, we will try creating an AI judge.