Course Curriculum

    1. Introduction

    2. Learning Objectives

    1. What is a Vector Database?

    2. Vector Embeddings

    3. Vector Space

    4. Quiz: Vector Database Fundamentals

    1. Vector Search

    2. Practice - Vector Search

    3. Types of Vector Searches

    4. Quiz: Vector Search

    1. ANN Algorithms

    2. Quiz: Approximate Nearest Neighbors

    1. Overview of the RAG System

    2. Chunking

    3. Filtering with Metadata

    4. Retrieval: Hybrid Search and Query Re-Writing

    5. Practice - Hybrid Search

    6. Fine-Tuned Embedding Models

    7. Quiz: Optimizing the RAG

    1. Scale, Reliability, and Cost

    2. Practice - Multi-Tenancy

    3. Practice - Vector Compression

    4. Quiz: Vector Databases in Production

About this course

  • Free
  • 42 lessons
  • 2 hours of video content