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graphRAG - Knowledge Graph-based RAG System Implementation with Neo4J (feat. LangChain)

Neo4J-based Knowledge Graph RAG System: Next-Generation Search Technology That Goes Beyond Simple Vector Search to Leverage Data Relationships Maximizing RAG Performance with the Power of Graph Databases!

(4.8) 39 reviews

349 learners

  • pdstudio
실습 중심
ai활용
RAG
LangChain
neo4j
LLM
DBMS/RDBMS

Reviews from Early Learners

What you will gain after the course

  • Methods for Building Knowledge Graphs Using Neo4J Graph Database

  • How to Apply Knowledge Graphs to RAG

  • Implementing a Graph-Based Search System by Integrating LangChain and Neo4J

  • Various search techniques (basic search, full-text search, vector search) utilization and integration

  • Text2Cypher Technique for Converting Natural Language Queries to Graph Queries

Utilizing Graph Databases for Powerful RAG Systems 🪄

Traditional RAG systems rely on simple vector search, making it difficult to properly represent relationships between information. GraphRAG utilizing graph databases structures complex relationships between data, enabling the generation of more accurate and contextually appropriate responses. Through structured knowledge representation and relationship-based search, you can achieve more accurate search results.

[Project 2] ETF Financial Product Recommendation - Knowledge Graph Implementation (Using Neo4J Browser)

Improving RAG Performance with Knowledge Graphs 🔧

Structured Knowledge Representation: Represents information as nodes and relationships to clarify connectivity between data and systematically structure complex knowledge.

Relationship-based Search: Provides more accurate results through search based on semantic relationships, going beyond simple keyword matching or vector similarity.

Integrated Multiple Search Methods: Maximizes search performance by combining keyword-based full-text search and vector-based semantic search.

Scalable Knowledge Structure: You can easily add and connect new data and relationships, enabling the construction of a continuously expandable knowledge base.

  • Natural data modeling is possible through nodes and relationships.

  • Intuitive graph exploration is possible through the Cypher query language.

  • It supports powerful graph algorithms and vector indexing.

  • It provides various features for integrating with the LangChain framework.

[Implementing RAG using LangChain + Neo4J] https://neo4j.com/labs/genai-ecosystem/langchain/

Features of This Course

Step-by-step learning focused on practical application

You will develop the ability to clearly understand and apply concepts of graph database and RAG integration by combining theoretical explanations with immediate hands-on practice.

Learning Various Real-World Case Studies

You will learn how to build and search knowledge graphs across various domains using real data such as movie recommendations, news data, ETF financial products, and legal documents.

You'll learn this kind of content

Neo4J Graph Database Fundamentals

Learn from Neo4J AuraDB setup to the core concepts of graph databases including nodes, relationships, and properties. Acquire the fundamental knowledge needed for utilizing graph databases, including major syntax of the Cypher query language, path traversal, and aggregation functions.

Knowledge Graph Construction

Learn how to transform data from various domains including CSV structured data, news, ETF financial products, and legal documents into knowledge graphs. Study ontology design, constraint setting, and how to utilize LangChain tools for graph transformation.

Graph-based Search Technique Implementation

You will learn how to implement various graph-based search techniques including basic search, full-text search, vector search (semantic search), and Text2Cypher. You will also learn hybrid search methods for implementing enhanced RAG systems.

Pre-enrollment Reference Information

Practice Environment

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

  • Virtual environment usage: Using uv package manager (conda, poetry, venv users can also follow along)

  • Tools used: VS Code, LLM API authentication key required (OpenAI/ Google Gemini) *Costs may apply

  • PC Specifications: Not applicable

  • Python 3.12 /

    langchain 0.3.23 / langchain-neo4j 0.3.0 / numpy 1.26.4

Learning Materials

  • Providing materials needed for practice (lecture notes, practice code, practice data)

  • Check the list of class notes and course materials available for download.

Prerequisites and Important Notes

  • Python basic programming skills

  • Understanding the Basic Concepts of LangChain

  • Graph database experience is not required (covered from the basics in the course)

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


Recommended for
these people

Who is this course right for?

  • Developers who want to improve the performance and accuracy of RAG systems

  • Those who want to effectively process data with complex relationships

  • Those who want to build structured knowledge search systems beyond simple vector search

  • Those who want to apply Knowledge Graphs to AI applications

Need to know before starting?

  • Python

  • LangChain

  • RAG

Hello
This is

14,971

Learners

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Reviews

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Answers

4.8

Rating

7

Courses

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[이력]

현) 핀테크 스타트업 CEO

전) 데이콘 CDO

전) 인덕대학교 컴퓨터소프트웨어학과 겸임교수

Kaggle Competitin Expert, 빅데이터 분석기사

 

[강의]

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Curriculum

All

58 lectures ∙ (7hr 4min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

39 reviews

4.8

39 reviews

  • sketchesfancy9795님의 프로필 이미지
    sketchesfancy9795

    Reviews 3

    Average Rating 5.0

    5

    31% enrolled

    I want to quickly try implementing in my DB.

    • pdstudio
      Instructor

      Thank you.

  • jyuri0018155님의 프로필 이미지
    jyuri0018155

    Reviews 1

    Average Rating 5.0

    5

    31% enrolled

    • kmkang2281님의 프로필 이미지
      kmkang2281

      Reviews 6

      Average Rating 4.8

      5

      31% enrolled

      Thank you sincerely.

    • hshin25375075님의 프로필 이미지
      hshin25375075

      Reviews 5

      Average Rating 4.2

      5

      31% enrolled

      • adastra01190030님의 프로필 이미지
        adastra01190030

        Reviews 1

        Average Rating 5.0

        5

        100% enrolled

        After using vectorRAG, I wanted to learn about graphRAG, and fortunately a course became available, so I took it. It was beneficial as I could follow along and learn through the Kind practical files, from explanations of basic concepts, data processing using real data, various query search methods... up to Hybrid RAG! There was something I actually wanted to implement, and after taking this course, I think I can immediately start using the knowledge I learned. The instructor's tone was calm and flowed smoothly, so even when listening at 1.7x speed, it wasn't bothersome and was easy to understand, allowing me to complete the course quickly! I enjoyed the course very much, instructor. I look forward to your next course as well!

      $110.00

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