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Level 4: Vector Databases for Scalable Features

Continue your Evolve42 journey by learning to use vector databases to power scalable AI features. Master embeddings and similarity search to build efficient, user-focused applications integrated with Blazor.

Macro View: Why Vector Databases Matter

Vector databases are a new type of database that is specifically designed for AI applications. They allow you to store and query high-dimensional vectors, which are mathematical representations of data like text, images, and audio. This makes them ideal for powering AI features like semantic search, recommendation systems, and image search.

What You'll Achieve in This Level

By the end of this level, you will:

Understand the fundamentals of vector databases and how they work.

Learn how to create and manage embeddings for different types of data.

Get an overview of popular vector database providers like Pinecone, Weaviate, and Milvus.

Learn how to use vector databases to build scalable AI features like semantic search and recommendation systems.

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Practice: Try AI in Action

Try the following hands-on task:

Create embeddings for a small dataset of text.

Use a vector database to find the most similar items to a given query.

Experiment with different embedding models and similarity metrics to see how the results change.

Reflect: What did you learn about how to use vector databases to power AI features?

Expand: Broaden Your Perspective

Understand how others are using vector databases in the real world:

Pinterest uses a vector database to power its visual search feature.

Spotify uses a vector database to power its music recommendation system.

Airbnb uses a vector database to power its similar listing feature.

These examples show that vector databases are a powerful tool for building a wide variety of AI-powered products.

Explore: Dive Deeper

Explore the tools shaping vector database’s frontier:

Pinecone: A fully managed vector database that is easy to use and scale.

Weaviate: An open-source vector search engine with a GraphQL API.

Milvus: An open-source vector database for production-ready AI applications.

These resources offer a hands-on path for those ready to experiment or build their own AI-enhanced systems.

Review Summary

Key Takeaways:

Vector databases are a new type of database that is specifically designed for AI applications.

They allow you to store and query high-dimensional vectors, which are mathematical representations of data like text, images, and audio.

This makes them ideal for powering AI features like semantic search, recommendation systems, and image search.

Connection to Macro View:

This level has equipped you with the skills to use vector databases to build scalable AI features. This is a key step in building AI-powered products that can handle large amounts of data and users.

Lead-In to Level 5:

Now that you know how to use vector databases, it's time to learn about how to deploy your AI models to production. In Level 5, you'll learn about serving efficiency and how to optimize your models for performance and scalability.

Continue Your Journey

Mastered vector databases? Move to Level 5 to learn about serving efficiency.

Go to Level 5

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