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.

Vector Databases- from Embeddings to Applications.png

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

Recap what you've learned in this course.

Conclusion

Before Getting Started

Getting Started with Vector Databases and Embeddings

Working with Vector Databases

Developing a Music Recommendation System

Wrapping Up

Conclusion