Course curriculum

    1. Introduction

    2. Learning Objectives

    1. Introduction

    2. What is a Vector Database?

    3. Vector Embeddings

    4. Vector Space

    5. Quiz

    1. Introduction

    2. Vector Search

    3. Types of Vector Searches

    4. Quiz

    1. Introduction

    2. Approximate Nearest Neighbors

    3. Quiz

    1. Introduction

    2. Overview of the RAG System

    1. Introduction

    2. Chunking

    3. Filtering with Metadata

    4. Retrieval: Hybrid Search and Query Re-Writing

    5. Fine-Tuned Embedding Models

    6. Quiz

About this course

  • $100.00
  • 40 lessons
  • 2 hours of video content

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