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.

🔄   Reset User Workspace

If you need to reset your workspace to its original state, follow these quick steps:

1:   Access the Menu: Look for the three-dot menu (⋼) in the top-right corner of the notebook toolbar.

2:   Restore Original Version: Click on "Restore Original Version" from the dropdown menu.

For more detailed instructions, please visit our Reset Workspace Guide.

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

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DeepLearning.AI
Short CourseIntermediate

Multi-vector Image Retrieval

Instructor: Kacper Ɓukawski

Qdrant logo
  • Intermediate
  • 7 Video Lessons
  • 5 Code Examples
  • Instructor: Kacper Ɓukawski
    • Qdrant
    Qdrant

What you'll learn

  • Understand multi-vector retrieval and how representing images as collections of patch-level embeddings enables more accurate search than single-vector methods.

  • Explore the ColPali model for image retrieval and apply optimization techniques like quantization and pooling to reduce memory usage while maintaining search quality.

  • Build a production multi-modal RAG system combining ColPali retrieval with the MUVERA model’s efficient search to handle complex documents mixing text and images.

About this course

Join our new short course, Multi-Vector Image Retrieval! Learn from Kacper Ɓukawski, Senior Developer Advocate at Qdrant.

Most retrieval systems represent each image with a single vector, but multi-vector techniques represent images as collections of smaller vectors—one for each patch. This detailed representation enables fine-grained matching between text query tokens and image patches, delivering higher-quality search on complex documents that combine text, images, and diagrams.

In this course, you’ll learn multi-vector retrieval concepts, implement ColPali for image search, and apply optimization techniques to make these systems production-ready. You’ll work with real course materials to build a complete multi-modal RAG system.

In detail, you’ll:

  • Learn multi-vector text retrieval with ColBERT – Understand how multi-vector retrieval works by implementing ColBERT for text, then explore the computational and memory challenges of late interaction search.
  • Implement ColPali for multi-vector image retrieval – Apply multi-vector concepts to images using ColPali, which adapts vision language models to create patch-level embeddings for fine-grained visual search.
  • Optimize ColPali’s memory footprint – Apply scalar and binary quantization, plus row, column, and hierarchical pooling techniques to dramatically reduce memory usage while preserving search quality.
  • Enable fast search with MUVERA embeddings – Convert multi-vector representations into high-dimensional single vectors using MUVERA, unlocking HNSW search for significantly faster retrieval.
  • Build a multi-modal RAG system with ColPali – Combine ColPali retrieval, memory optimizations, and MUVERA search into a complete RAG pipeline that retrieves and reasons over visual documents.

Start building retrieval systems that understand images at the patch level and deliver accurate multi-modal search.

Who should join?

AI builders working with multi-modal data who want to implement advanced image retrieval. Familiarity with Python and vector embeddings is recommended.

Course Outline

7 Lessons・5 Code Examples

  • Introduction

    Video・3 mins

  • Multi-vector Text Retrieval: ColBERT

    Video with Code Example・17 mins

  • Multi-vector Image Retrieval: ColPali

    Video with Code Example・15 mins

  • Optimizing retrieval with multi vector representations

    Video with Code Example・15 mins

  • MUVERA Embeddings

    Video with Code Example・18 mins

  • Building multi-modal RAG with ColPali

    Video with Code Example・11 mins

  • Conclusion

    Video・1 min

  • Quiz

    Graded・Quiz

    ・10 mins

Instructor

Kacper Ɓukawski

Kacper Ɓukawski

Developer Relations Lead at Qdrant

Additional learning features, such as quizzes and projects, are included with DeepLearning.AI Pro. Explore it today

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