Quick Guide & Tips

đŸ’»   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.

đŸ’»   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)"

đŸ’»   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).

đŸ“±   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.

đŸ–„   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.

📚   Enroll in Other Short Courses

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. 👇

👉👉 🔗 DeepLearning.AI – All Short Courses [+]

🙂   Let Us Know What You Think

Your feedback helps us know what you liked and didn't like about the course. We read all your feedback and use them to improve this course and future courses. Please submit your feedback by clicking on "Course Feedback" option at the bottom of the lessons list menu (desktop view).

Also, you are more than welcome to join our community 👉👉 🔗 DeepLearning.AI Forum

Sign in

Create Your Account

Or, sign up with your email
Email Address

Already have an account? Sign in here!

By signing up, you agree to our Terms Of Use and Privacy Policy

Choose Your Learning Path

MonthlyYearly

Change Your Plan

Your subscription plan will change at the end of your current billing period. You’ll continue to have access to your current plan until then.

View All Plans and Features

Manage Your Learning Plan

MonthlyYearly

Change Your Plan

Your subscription plan will change at the end of your current billing period. You’ll continue to have access to your current plan until then.

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 CourseBeginner1 hour 33 mins

Retrieval Optimization: Tokenization to Vector Quantization

Instructor: Kacper Ɓukawski

Qdrant logo
  • Beginner
  • 1 hour 33 mins
  • 8 Video Lessons
  • 6 Code Examples
  • Instructor: Kacper Ɓukawski
    • Qdrant
    Qdrant

What you'll learn

  • Learn how tokenization works in large language and embedding models and how the tokenizer can affect the quality of your search.

  • Explore how different tokenization techniques including Byte-Pair Encoding, WordPiece, and Unigram are trained and work.

  • Understand how to measure the quality of your retrieval and how to optimize your search by adjusting HNSW parameters and vector quantizations.

About this course

In Retrieval Optimization: From Tokenization to Vector Quantization, taught by Kacper Ɓukawski, Developer Relations Lead of Qdrant, you’ll learn all about tokenization and also how to optimize vector search in your large-scale customer-facing RAG applications. You’ll explore the technical details of how vector search works and how to optimize it for better performance.

This course focuses on optimizing the first step in your RAG and search results. You’ll see how different tokenization techniques like Byte-Pair Encoding, WordPiece, and Unigram work and how they affect search relevancy. You’ll also learn how to address common challenges such as terminology mismatches and truncated chunks in embedding models.

To optimize your search, you need to be able to measure its quality. You will learn several quality metrics for this purpose. Most vector databases use Hierarchical Navigable Small Worlds (HNSW) for approximate nearest-neighbor search. You’ll see how to balance the HNSW parameters for higher speed and maximum relevance. Finally, you would use different vector quantization techniques to enhance memory usage and search speed.

What you’ll do, in detail:

  • Learn about the internal workings of the embedding model and how your text is turned into vectors.
  • Understand how several tokenizers such as Byte-Pair Encoding, WordPiece, Unigram, and SentencePiece are trained.
  • Explore common challenges with tokenizers such as unknown tokens, domain-specific identifiers, and numerical values, that can negatively affect your vector search.
  • Understand how to measure the quality of your search across several quality metrics
  • Understand how the main parameters in HNSW algorithms affect the relevance and speed of vector search and how to optimally adjust these parameters.
  • Experiment with the three major quantization methods, product, scalar, and binary, and learn how they impact memory requirements, search quality, and speed.

By the end of this course, you’ll have a solid understanding of how tokenization is done and how to optimize vector search in your RAG systems.

Who should join?

Anyone with basic Python knowledge who wants to learn to build effective customer-facing RAG applications!

Course Outline

8 Lessons・6 Code Examples

  • Introduction

    Video・6 mins

  • Embedding models

    Video with Code Example・16 mins

  • Role of the tokenizers

    Video with Code Example・15 mins

  • Practical implications of the tokenization

    Video with Code Example・14 mins

  • Measuring Search Relevance

    Video with Code Example・14 mins

  • Optimizing HNSW search

    Video with Code Example・10 mins

  • Vector quantization

    Video with Code Example・16 mins

  • Conclusion

    Video・1 min

  • Quiz

    Graded・Quiz

    ・10 mins

Instructor

Kacper Ɓukawski

Kacper Ɓukawski

Developer Relations Lead at Qdrant

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

Want to learn more about Generative AI?

Keep learning with updates on curated AI news, courses, and events, as well as Andrew’s thoughts from DeepLearning.AI!