Redis + AI: Building a Vector Database with Redis

Introduction

• Introduction
• What you should know

1. Understanding Data

• What is structured data and where does it come from?
• What is unstructured data and where does it come from?
• Using structured data
• Using unstructured data: Use case examples
• Which is better? Structured vs. unstructured data
• Unstructured to structured data
• Practical example: Metadata

2. AI-Optimized Databases

• What are AI-optimized databases?
• What are vector databases?
• What are embeddings?
• How do vector databases work?
• Examples of use cases for vector databases

3. Enter Redis

• Quick introduction of RediSearch and how to get it
• Redis used as a vector database
• Using Redis as a recommendation engine: Architecture overview
• Using Redis as a documentation retrieval database: Architecture review

4. Building an Image Similarity Search

• Introduction to the project to solve
• Architecture review: Using Redis as a vector database
• Tech stack overview
• Implementation overview: A deep dive into the main aspects of the implementation

5. Building a Semantic Search Example

• Introduction to the project to solve
• Architecture review: Using Redis as a vector database
• Tech stack overview
• Implementation video

Conclusion

• Next steps