In this hands-on tutorial, we’ll introduce embeddings and vector search from both an ML- and application-level perspective. We’ll start with a high-level overview of embeddings and discuss best practices around embedding generation and usage. Then we’ll use this knowledge to build a semantic text search application. Finally, we’ll see how we can put our application into production using Milvus, the world’s most popular open-source vector database.

What you’ll need:
- Python 3.9 or above
- A basic understanding of vectors and databases

What you’ll learn:
- What is vector database
- What is semantic similarity
- How to use a vector database to find similar texts

# What will you learn?
In this hands-on tutorial, we’ll introduce embeddings and vector search from both an ML- and application-level perspective. We’ll start with a high-level overview of embeddings and discuss best practices around embedding generation and usage. 

Then we’ll use this knowledge to build a semantic text search application. Finally, we’ll see how we can put our application into production using Milvus, the world’s most popular open-source vector database.

## What you’ll need:
- Python 3.9 or above
- A basic understanding of vectors and databases

## What you’ll learn:
- What is vector database
- What is semantic similarity
- How to use a vector database to find similar texts


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Training

Get hands-on in this live tutorial where you'll learn how to use a vector database to build a semantic text search application.

Tutorial: Building a Semantic Text Search Application | Zilliz Webinar

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Tutorial: Building a Semantic Text Search Application

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