This essential guide provides you with all the information you need to understand vector databases and their use in generative AI like LLM.

Unlike traditional databases, vector databases excel at detecting similarities between data objects, making them indispensable for tasks such as movie recommendations and AI-driven insights.

What you will learn:

1. Understanding traditional databases: Learn how SQL databases organize data into rows and columns and the limitations of fuzzy queries.
2. Introduction to Vector Databases: Discover how data is represented as multidimensional vectors, capturing the essence of text, images, etc.
3. Indexing and querying: See how vector databases use algorithms such as word embeddings to transform data into vectors for efficient storage and retrieval.
4. Real-world applications: Explore a practical example of recommending movies based on vector similarities.
5. Advanced concepts: Dive into vector storage, indexing algorithms, distance metrics, and query processing engines.
6. The Future of AI with RAG: Understand how vector databases improve retrieval augmented generation (RAG) for contextually relevant AI answers.

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