Short CourseIntermediate0 Hours 55 Minutes

Vector Databases: from Embeddings to Applications

Instructors: Sebastian Witalec

Weaviate
  • Intermediate
  • 0 Hours 55 Minutes
  • 8 Video Lessons
  • 6 Code Examples

What you'll learn

  • Build efficient, practical applications including hybrid and multilingual searches, for diverse industries.

  • Understand vector databases and use them to develop GenAI applications without needing to train or fine-tune an LLM yourself.

  • Learn to discern when best to apply a vector database to your application.

About this course

Vector databases play a pivotal role across various fields, such as natural language processing, image recognition, recommender systems and semantic search, and have gained more importance with the growing adoption of LLMs. 

These databases are exceptionally valuable as they provide LLMs with access to real-time proprietary data, enabling the development of Retrieval Augmented Generation (RAG) applications.

At their core, vector databases rely on the use of embeddings to capture the meaning of data and gauge the similarity between different pairs of vectors and sift through extensive datasets, identifying the most similar vectors. 

This course will help you gain the knowledge to make informed decisions about when to apply vector databases to your applications. You’ll explore:

  • How to use vector databases and LLMs to gain deeper insights into your data.
  • Build labs that show how to form embeddings and use several search techniques to find similar embeddings.
  • Explore algorithms for fast searches through vast datasets and build applications ranging from RAG to multilingual search.

Who should join?

Anyone who’s interested in understanding and applying vector databases in their applications.

Course Outline

8 Lessons・6 Code Examples
  • Introduction

    Video3 mins

  • How to Obtain Vector Representations of Data

    Video with code examples11 mins

  • Search for Similar Vectors

    Video with code examples6 mins

  • Approximate nearest neighbours

    Video with code examples14 mins

  • Vector Databases

    Video with code examples7 mins

  • Sparse, Dense, and Hybrid Search

    Video with code examples6 mins

  • Application - Multilingual Search

    Video with code examples6 mins

  • Conclusion

    Video1 min

Instructor

Sebastian Witalec

Sebastian Witalec

Head of Developer Relations at Weaviate

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

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