RAG System Implemented with AI Agents (w. LangGraph)

Building 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.

(4.8) 141 reviews

7,831 learners

Level Basic

Course period Unlimited

AI Agent
AI Agent
LangGraph
LangGraph
RAG
RAG
LLM
LLM
LangChain
LangChain
AI Agent
AI Agent
LangGraph
LangGraph
RAG
RAG
LLM
LLM
LangChain
LangChain

Reviews from Early Learners

Reviews from Early Learners

4.8

5.0

이성규

61% enrolled

This is a well-organized and neat lecture. I hesitated to purchase it since I already had experience with the LangGraph project, but the content is so neatly organized and explained calmly step-by-step that I learned things I didn't know before and don't regret buying it at all. I'm seriously considering buying the RAG Langchain lecture because of this lecture.

5.0

조영재

31% enrolled

The course content was very easy to understand and highly helpful as it can be applied directly to practical work. The explanations were so clear that even difficult concepts felt natural to grasp.

5.0

jiyoung lim

98% enrolled

Okay

What you will gain after the course

  • AI Agent Structure Design and Implementation Using LangGraph

  • Applying AI Agents to RAG (Retrieval-Augmented Generation)

  • Expanding AI Agent Capabilities by Implementing Tool Calling Features

  • Mastering the latest Agentic RAG architectures, including Adaptive RAG, Self-RAG, and Corrective RAG

A magic tool for a powerful RAG system,
the AI Agent 🪄

While LLMs excel at natural language processing and text generation, they have limitations in independently coordinating complex workflows or making decisions. Beyond simple information retrieval, LLMs need the help of agents to evaluate results, modify queries, and select appropriate tools for the situation. For a smarter and more flexible RAG system, agents are an essential technology to understand.

AI Agent that enhances LLM performance 🔧 🔧

Selecting the right tools for the situation

It helps the LLM decide which tools to use according to the situation. Depending on the user's requested task, it can select appropriate tools such as API calls, database searches, or external searches.

Generating optimal search queries

When a user's question is unclear or complex, it helps to refine or modify the query to obtain more accurate results. Through this process, the LLM generates the optimal search query.

Deriving high-quality answers

When multiple results are returned, it evaluates the most relevant information and selects the optimal answer. This allows for providing accurate information to the user.

Determining follow-up actions for result improvement

If the results are insufficient or inaccurate, it determines whether additional work is necessary and executes a feedback loop to repeat the search or attempt a new approach.

Why use LangGraph? 🤔

LangGraph is an advantageous tool for implementing complex workflows. While LangChain is suitable for handling relatively fixed flows, LangGraph is a great match for agents because it can flexibly process and manage complex tasks.

<Characteristics of LangGraph>

  • Various states and conditions can be easily handled through node-based management.

  • Complex workflows can be managed visually.

  • By combining agents with LangGraph, you can effectively connect &amp; execute various modules.


Features of this course

Practical, step-by-step learning

By conducting hands-on practice immediately after theoretical explanations, you will develop a solid understanding of concepts and the ability to apply them.

Curriculum reflecting the latest trends

By actively incorporating the latest technologies and methodologies in agent-based RAG, we provide knowledge that can be immediately applied in the field.

The Complete Guide to LangGraph

We explain complex LangGraph step-by-step from the basics so that anyone can understand, and provide in-depth learning through various real-world examples.

Easy to review with provided tutorials

We provide a Wikidocs textbook summarizing LangGraph and Agentic RAG, allowing for continuous learning and reference even after completing the course.

What you will learn

Designing AI Agent Flows with LangGraph

You will learn the core concepts of LangGraph, including state graphs, conditional edges, and feedback loops, and understand how to model complex AI agent decision-making processes as graphs. Additionally, you will learn techniques applicable to various AI agent projects, such as Human-in-the-Loop, parallel execution, and sub-graphs.

Expanding AI Capabilities with Tool Calling

Master the Tool Calling technology that connects the capabilities of AI agents with the real world. We cover how to directly create and call LangChain's built-in tools and custom tools. You will learn how to integrate external APIs and various tools into AI systems.

Implementing Agent-Based Advanced RAG Techniques

Explore advanced techniques to take RAG system performance to the next level. Learn the concepts and implementation techniques of Adaptive RAG, which operates dynamically based on context, as well as Self RAG and Corrective RAG, which allow AI to evaluate and improve its own output.

Notes before taking the course

Practice Environment

  • Operating System and Version (OS): Lectures are conducted based on MacOS (Linux and Windows users can also participate in the practice)

  • Virtual Environment Usage: Lectures are conducted based on Poetry (conda and venv users can also follow along).

  • Tools used: VS Code, OpenAI API, etc. LLM authentication keys required (additional costs may apply)

  • PC Specs: N/A

  • LangGraph version: v0.2.34 applied

  • LangChain version: v0.3.1 applied

Learning Materials

Prerequisites and Important Notes

  • Those with basic knowledge of Python (those capable of basic programming)

  • [Free Lecture] LangChain Basics for Beginners (Required) : https://inf.run/Xabb2


  • If you have any questions or opinions, please feel free to ask.

Related Course Information

  • RAG Master: From Basics to Advanced Techniques (feat. LangChain)

  • From RAG implementation to performance evaluation -

    Practical AI Development in 9 Hours

    • Hands-on Practice: Building a LangChain-based RAG System

    • Learning Advanced RAG Techniques

    • RAG System Performance Evaluation Methodology

    • Latest LCEL syntax and Runnable usage in LangChain


  • Link: https://inf.run/CxVA3

Recommended for
these people

Who is this course right for?

  • Those who want to go beyond chatbots and create their own intelligent AI agents

  • Those who want to take on the challenge of developing practical AI solutions using RAG and LLM

  • Those who want to take the next step after completing a LangChain-based 'Chatbot' or 'RAG' course

Need to know before starting?

  • Python

  • (Free Lecture) LangChain Basics for Beginners [Required]

  • (Paid Course) RAG Master: From Basics to Advanced Techniques [Recommended]

Hello
This is pdstudio

16,851

Learners

834

Reviews

168

Answers

4.8

Rating

10

Courses

Hello. I am currently working in the field of data analysis and AI service development using Python. I have been consistently writing books and delivering AI lectures to share the topics I study with as many people as possible.

[Experience] Current) CEO of a Fintech Startup Former) CDO at Dacon Former) Adjunct Professor, Department of Computer Software, Induk University Kaggle Competition Expert, Big Data Analysis Engineer [Lectures] NCS Registered Instructor

[Experience]

Current) CEO of a Fintech Startup

Former CDO at DACON

Former Adjunct Professor, Department of Computer Software, Induk University

Kaggle Competition Expert, Big Data Analysis Engineer

[Lectures] NCS Registered Instructor (Artificial Intelligence) Selected as an 'Outstanding Partner' for SBA (Seoul Business Agency) SeSAC Campus SW Education (AI Model Development using Python) Financial Security Institute, Korea Electronics

[Lectures]

NCS Registered Instructor (Artificial Intelligence)

Selected as an 'Outstanding Partner' for SW Education at the Seoul Business Agency (SBA) SeSAC Campus (AI Model Development using Python)

Lectures at Financial Security Institute, Korea Electronics Association (KEA), Korea Display Industry Association (KDIA), Daegu Digital Industry Promotion Agency (DIP), etc.

Experience in providing education at major domestic universities such as Seoul National University, Pusan National University, Kyung Hee University, and Hankuk University of Foreign Studies, as well as for domestic corporations

[Writing] Python Machine Learning Pandas Data Analysis (InfoBook): https://zrr.kr/x1ec Python Deep Learning Machine Learning Introduction (InfoBook): https://zrr.kr/RPaE Python Deep Learning Ten

[Authoring]

[YouTube] Pandas Studio : https://youtube.com/@pandas-data-studio?si=XoLVQzJ9mmdFJQHU

More

Curriculum

All

54 lectures ∙ (6hr 45min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

141 reviews

4.8

141 reviews

  • qkenr1321559님의 프로필 이미지
    qkenr1321559

    Reviews 6

    Average Rating 5.0

    5

    61% enrolled

    This is a well-organized and neat lecture. I hesitated to purchase it since I already had experience with the LangGraph project, but the content is so neatly organized and explained calmly step-by-step that I learned things I didn't know before and don't regret buying it at all. I'm seriously considering buying the RAG Langchain lecture because of this lecture.

    • pdstudio
      Instructor

      Thank you so much! 😊 I'll prepare an even better lecture for you next time! 🌟

  • kjkwiner3904님의 프로필 이미지
    kjkwiner3904

    Reviews 3

    Average Rating 4.7

    5

    31% enrolled

    • kjsgo070727272님의 프로필 이미지
      kjsgo070727272

      Reviews 2

      Average Rating 5.0

      5

      65% enrolled

      • hhgwak5616님의 프로필 이미지
        hhgwak5616

        Reviews 2

        Average Rating 5.0

        5

        61% enrolled

        • namiezexx5494님의 프로필 이미지
          namiezexx5494

          Reviews 5

          Average Rating 5.0

          5

          31% enrolled

          pdstudio's other courses

          Check out other courses by the instructor!

          Similar courses

          Explore other courses in the same field!

          $77.00