Vector Database Fundamentals

Dive into the world of vector databases and Retrieval Augmented Generation (RAG) with our comprehensive KDB AI course. Learn how to efficiently store, search, and retrieve high-dimensional data using cutting-edge techniques.

Key topics include:

• Vector search fundamentals and applications

• Advanced metadata filtering

• Implementing RAG pipelines to enhance AI applications

• Choosing and optimizing embedding models

• Mastering similarity metrics: Euclidean distance, cosine similarity, and dot product

• Leveraging indexes like HNSW and IVF-PQ for improved performance

• Building sophisticated query systems with metadata filtering

Practical demonstrations cover:

• Creating and managing tables

• Implementing a RAG pipeline from scratch

• Using metadata filters to make complex queries with groupings and aggregations

Some questions you will be able to answer after this course:

• How do I choose an index? What are the right algorithm parameters for my data?

• How do I choose an embedding model?

• How do I optimize RAG performance?

• How do I use a vector database to gain insights from my unstructured data

  
  

Whether you're a data scientist, ML engineer, or AI enthusiast, this course equips you with the skills to create powerful AI-driven applications. Learn to combine vector search with large language models, optimize query performance, and solve real-world problems across various industries.

Join us to unlock the full potential of semantic search and RAG with KDB AI Vector Database!

Gain hands-on experience with KDB AI Cloud instances. Master the intricacies of vector embeddings and learn to build scalable, efficient AI systems that push the boundaries of intelligent search and generation.