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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vdbstreamlitnew.tar.gz

jupyter-wordembeddings

jupyter-sentenceembeddings

jupyter-documentembeddings

jupyter-imageembeddings

jupyter-videoembeddings

jupyter-audioembeddings

jupyter-multimodalembeddings

jupyter-multimodalembeddingsSearchbyText

jupyter-multimodalembeddingsSearchbyImage

jupyter-FineTuningCLIPonCustomData

jupyter-ChromaDBMultimodalEmbeddings

jupyter-MultimodalEmbeddingsWithFineTunedModel

jupyter-appsearchbytextandsearchbyimage

streamlit-musicrecommendationsystem

liveApp-streamlit-musicrecommendationsystem-both

liveApp-streamlit-musicrecommendationsystem-metadata

liveApp-streamlit-musicrecommendationsystem-audio

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

A database that stores different types of data in a numerical form, referred to as a vector

A database that only stores audio and video

A machine learning model that converts data objects into numerical vectors called embeddings

The semantic relationship and context of the data

**(Select all that apply.)** What do embeddings capture?


(Select all that apply.) What do embeddings capture?

It provides efficient storage, indexing, and retrieval of high-dimensional vector representations (embeddings) of data.

What role does a vector database play in supporting data-driven applications?

What role does a vector database play in supporting data-driven applications?

To organize vector data for efficient search

What is the function of the indexing layer in a vector database?

What is the function of the indexing layer in a vector database?

To handle incoming queries and orchestrate the search process

What is the role of the query processing layer in a vector database?

What is the role of the query processing layer in a vector database?

Which similarity measure would you choose for a recommendation system that prioritizes directional similarity over magnitude?

Which similarity measure would you choose for a recommendation system that prioritizes directional similarity over magnitude?

Fine-tuning and generating embeddings are both processes that involve training a model from scratch using large datasets.

Fine-tuning is used for images, while embeddings are used for text.

Fine-tuning involves adjusting the parameters of a pretrained model to a specific task while generating embeddings involves creating fixed-size vector representations of data.

Generating embeddings requires labeled data, whereas fine-tuning requires no labeled data.

What distinguishes fine-tuning from generating embeddings?



What distinguishes fine-tuning from generating embeddings?

Test your understanding of the concepts of vector databases and embeddings.

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: Getting Started with Vector Databases and Embeddings