Vector Database: A Must-Know Guide

This must-know guide will provide you with all the information you need to understand vector databases and how they are used in generative AI like LLM.

Unlike traditional databases, vector databases excel in identifying similarities between data objects, making them indispensable for tasks like movie recommendations and AI-driven insights.

What You'll Learn:

1. Understanding Traditional Databases: Learn how SQL databases organize data in rows and columns, and their limitations with fuzzy queries.
2. Introduction to Vector Databases: Discover how data is represented as multi-dimensional vectors, capturing the essence of text, images, and more.
3. Indexing and Querying: See how vector databases convert data into vectors for efficient storage and retrieval, using algorithms like word embeddings.
4. Real-World Applications: Explore a practical example of recommending movies using vector similarities.
5. Advanced Concepts: Delve into vector storage, indexing algorithms, distance metrics, and query processing engines.
6. Future of AI with RAG: Understand how vector databases enhance Retrieval Augmented Generation (RAG) for contextually relevant AI responses.

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Timestamps -
00:00 - Introduction
00:22 - Vector Database
01:01 - Indexing
01:12 - Querying
01:58 - Recommending Movies
03:04 - Core Components
03:07 - Vector Storage
03:31 - Vector Indexing
04:01 - Distance Metrics
04:24 - Query Processing Engine
04:39 - Additional Features
05:20 - RAG

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