Vector Databases: from Embeddings to Applications

Overview

Class Central Tips

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: 1. How to use vector databases and LLMs to gain deeper insights into your data. 2. Build labs that show how to form embeddings and use several search techniques to find similar embeddings. 3. Explore algorithms for fast searches through vast datasets and build applications ranging from RAG to multilingual search.

Syllabus

Project Overview

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: (1) How to use vector databases and LLMs to gain deeper insights into your data. (2) Build labs that show how to form embeddings and use several search techniques to find similar embeddings. (3) Explore algorithms for fast searches through vast datasets and build applications ranging from RAG to multilingual search.