This course teaches how data vectorization and vector databases enable context-based search over keyword matching, multimodal data search, enhance recommendation systems, and power LLMs.

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jupyter-appsearchbytextandsearchbyimage

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Vector databases transform how we search, analyze, and recommend data in today’s AI-driven world. These databases are at the heart of modern applications like semantic search, multimodal search, recommendation systems, and retrieval augmented generation (RAG) for large language models. By using embeddings, which are numerical representations that capture the meaning of data, vector databases allow us to find similar information quickly and accurately, even across vast datasets. This makes them essential for building intelligent systems that understand data beyond simple keyword matching.

In this vector databases course, you’ll learn to generate embeddings for various data types and use vector databases to store and query them. Using the power of embeddings and vector databases, you’ll build semantic search apps, recommendation systems, and multimodal search solutions. 

After completing this course, you’ll have the skills to determine when and how to effectively apply vector databases to different projects.

Vector Databases: From Embeddings to Applications

Faster retrieval times for structured data

Speed and accuracy in similarity matching

What is the key advantage of using vector databases for similarity search?

What is the key advantage of using vector databases for similarity search?

They cannot handle large volumes of data.

They are optimized for exact match and range queries, not similarity searches.

Why are traditional databases not well-suited for storing and querying vectorized data in modern AI-driven applications?

Why are traditional databases not well-suited for storing and querying vectorized data in modern AI-driven applications?

What indexing technique is commonly used by vector databases to optimize similarity search in high-dimensional spaces?

What indexing technique is commonly used by vector databases to optimize similarity search in high-dimensional spaces?

Which capability distinguishes vector databases from traditional databases in terms of querying?

Which capability distinguishes vector databases from traditional databases in terms of querying?

Which open-source vector database is designed for similarity search and integrates well with large language models (LLMs)?

Which open-source vector database is designed for similarity search and integrates well with large language models (LLMs)?

Which open-source vector database employs a multi-layered architecture and supports hybrid search capabilities combining vector similarity search with structured data filtering?



Which open-source vector database employs a multi-layered architecture and supports hybrid search capabilities combining vector similarity search with structured data filtering?

NSW graphs guarantee the shortest path in all searches.

NSW graphs provide faster query times by efficiently navigating through a small-world network structure.

NSW graphs are only effective for small datasets.

NSW graphs guarantee exact matches in similarity search.

What is the advantage of navigable small world (NSW) graphs in similarity search?

What is the advantage of navigable small world (NSW) graphs in similarity search?

How does the hierarchical navigable small world (HNSW) improve upon NSW graphs?

How does the hierarchical navigable small world (HNSW) improve upon NSW graphs?

**(True or False)** Product Quantization (PQ) is a technique to compress vectors into compact codes to speed up similarity searches in high-dimensional spaces.


(True or False) Product Quantization (PQ) is a technique to compress vectors into compact codes to speed up similarity searches in high-dimensional spaces.

Test your understanding of the open-source vector databases.

Before Getting Started

Getting Started with Vector Databases and Embeddings

Working with Vector Databases

Developing a Music Recommendation System

Wrapping Up

Semantic Search with Transformers

Quiz: Working with Vector Databases