This must-know guide will give you 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 at identifying similarities between data objects, making them indispensable for tasks like movie recommendations and AI-driven insights.

What you learn:

1. Understanding Traditional Databases: Learn how SQL databases organize data into 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 retrieval: See how vector databases convert data into vectors for efficient storage and retrieval, using algorithms such as word embedding.
4. Real World Applications: Discover a practical example of recommending movies using vector matching.
5. Advanced Concepts: Dive into vector storage, indexing algorithms, distance statistics, and query processing engines.
6. Future of AI with RAG: Understand how vector databases improve Retrieval Augmented Generation (RAG) for contextually relevant AI responses.

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