Project
Further steps with Qdrant
14 completions
~ 15 hours
3.9Before selecting this project, it is recommended that first complete the Vector database with Qdrant.
You will be able to adjust the search parameters for retrieval speed and accuracy, learn about index configuration, and quantize the embeddings to optimize the database storage.
Provided by
JetBrains Academy
About
This project demonstrates the optimizations of Qdrant, a vector-first database, showcasing how to understand the search quality, balance search speed and accuracy, modify the index, and quantize the stored embeddings.
Training project
This project allows you to practice and strengthen your coding skills, helping you get ready for more advanced tasks ahead.
What you'll learn
Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:
Perform the approximate and the exact k-Nearest Neighbor search to observe their speeds and accuracy
Inspect one of the search parameters to understand how it affects the query
Learn about quantization, a technique for compressing embeddings to retain accuracy and minimize query speed
Reviews
4 months ago
This is a good project for learning basic optimalizations in Qdrant. Currently, the project has some initial issues but still it is good and valuable project.
Daniel Wirth
7 months ago
I have learned what optimisation qdrant offers and the impact on searches and execution time.
9 months ago
It was useful to do tasks to improve understanding in working with Qdrant and embedding.
3.9
Learners who completed this project within the course rated it as follows: