[Free] Vector Database Fundamentals
Mastering RAG: Vector Search, Embeddings, and LLM Integration – Free Course
What you’ll learn
- Implement Retrieval Augmented Generation (RAG) using KDB AI and OpenAI, including setting up a complete RAG pipeline.
- Gain hands-on experience in data preparation, embedding generation, vector database operations, and integration with language models.
- Master vector search techniques, advanced vector operations, and querying methods for efficient information retrieval.
- Explore practical applications of RAG in AI-powered systems and NLP projects.
Requirements
- Basic knowledge of Python programming. Familiarity with machine learning and NLP concepts is helpful. A KDB AI account (free tier) and OpenAI API access are recommended for exercises.
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.
Author(s): Michael Ryaboy
