Quick Guide & Tips

💻   Accessing Utils File and Helper Functions

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.

💻   Downloading Notebooks

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Download as"

3:   Then, click on "Notebook (.ipynb)"

💻   Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.

📗   See Your Progress

Once you enroll in this course—or any other short course on the DeepLearning.AI platform—and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).

📱   Features to Use

🎞   Adjust Video Speed: Click on the gear icon (⚙) on the video and then from the Speed option, choose your desired video speed.

🗣   Captions (English and Spanish): Click on the gear icon (⚙) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

🔅   Video Quality: If you do not have access to high-speed internet, click on the gear icon (⚙) on the video and then from Quality, choose the quality that works the best for your Internet speed.

🖥   Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

√   Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.

🧑   Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

🧑   Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

📅   Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

☕   Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

💬   Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

✍   Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.

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Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. 👇

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Welcome back!

Hi ,

We'd like to know you better so we can create more relevant courses. What do you do for work?

DeepLearning.AI
Short CourseIntermediate1 hour 54 mins

Knowledge Graphs for RAG

Instructor: Andreas Kollegger

Neo4j logo
  • Intermediate
  • 1 hour 54 mins
  • 9 Video Lessons
  • 6 Code Examples
  • Instructor: Andreas Kollegger
    • Neo4j
    Neo4j

What you'll learn

  • Use Neo4j’s query language Cypher to manage and retrieve data stored in knowledge graphs.

  • Write knowledge graph queries that find and format text data to provide more relevant context to LLMs for Retrieval Augmented Generation.

  • Build a question-answering system using Neo4j and LangChain to chat with a knowledge graph of structured text documents.

About this course

Knowledge graphs are used in development to structure complex data relationships, drive intelligent search functionality, and build powerful AI applications that can reason over different data types. Knowledge graphs can connect data from both structured and unstructured sources (databases, documents, etc.), providing an intuitive and flexible way to model complex, real-world scenarios.

Unlike tables or simple lists, knowledge graphs can capture the meaning and context behind the data, allowing you to uncover insights and connections that would be difficult to find with conventional databases. This rich, structured context is ideal for improving the output of large language models (LLMs), because you can build more relevant context for the model than with semantic search alone.

This course will teach you how to leverage knowledge graphs within retrieval augmented generation (RAG) applications. You’ll learn to:

  • Understand the basics of how knowledge graphs store data by using nodes to represent entities and edges to represent relationships between nodes.
  • Use Neo4j’s query language, Cypher, to retrieve information from a fun graph of movie and actor data.
  • Add a vector index to a knowledge graph to represent unstructured text data and find relevant texts using vector similarity search.
  • Build a knowledge graph of text documents from scratch, using publicly available financial and investment documents as the demo use case
  • Explore advanced techniques for connecting multiple knowledge graphs and using complex queries for comprehensive data retrieval.
  • Write advanced Cypher queries to retrieve relevant information from the graph and format it for inclusion in your prompt to an LLM.

After course completion, you’ll be well-equipped to use knowledge graphs to uncover deeper insights in your data, and enhance the performance of LLMs with structured, relevant context.

Who should join?

Anyone who wants to understand how knowledge graphs work, how to build with them, and create better RAG applications. We recommend familiarity with LangChain or taking LangChain: Chat with Your Data prior to this course.

Course Outline

9 Lessons・6 Code Examples

  • Introduction

    Video・5 mins

  • Knowledge Graph Fundamentals

    Video・6 mins

  • Querying Knowledge Graphs

    Video with Code Example・19 mins

  • Preparing Text for RAG

    Video with Code Example・7 mins

  • Constructing a Knowledge Graph from Text Documents

    Video with Code Example・16 mins

  • Adding Relationships to the SEC Knowledge Graph

    Video with Code Example・17 mins

  • Expanding the SEC Knowledge Graph

    Video with Code Example・17 mins

  • Chatting with the Knowledge Graph

    Video with Code Example・23 mins

  • Conclusion

    Video・1 min

  • Quiz

    Graded・Quiz

    ・10 mins

Instructor

Andreas Kollegger

Andreas Kollegger

Lead for GenAI Innovation at Neo4j

Course access is free for a limited time during the DeepLearning.AI learning platform beta!

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