Gain insights into generating embeddings and using vector databases to enhance search and recommendation systems. Delve into multimodal embeddings, Chroma, and performance optimization techniques like HNSW.

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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

In this course, you’ll learn to generate embeddings and utilize vector databases to build semantic search apps, enhance recommendation systems, and develop multimodal search solutions.

You’ll begin by understanding the concept of embeddings, vector databases, and their importance in modern world applications. You’ll learn to generate text embeddings with BERT, image and video embeddings with CNNs, audio embeddings with mel spectrogram, and multimodal embeddings with CLIP. You’ll explore the architecture, design choices, and key features of different open-source vector databases, focusing on using Chroma for storage and queries on multimodal data. You’ll learn performance optimization techniques, especially HNSW. You’ll also learn to develop applications, including similarity search applications for images, videos, and audio.

By the end of this course, you will have a solid foundation in vector databases and be able to apply your knowledge to small—and large-scale 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

Quiz: Getting Started with Vector Databases and Embeddings