Vector Database Fundamentals, Mastering RAG: Vector Search, Embeddings, and LLM Integration.
Course Description
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.
