Welcome to "Introduction to Vector Indexes and Unstructured Data".
In this course, you will explore the concepts of semantic search, embedding, and vectors. You will learn how to process and index unstructured data, and use vector indexes to search for similar items.
You will explore unstructured datasets and how they can be modeled and indexed in a graph database.
You will create a graph of the unstructured course content from GraphAcademy, extract metadata from the content, and use Cypher to query the data.
You will use Python, LangChain, Neo4j, and OpenAI to process, embed, and index the data.
What you need
To complete the practical activities in this course, you will need:
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A Neo4j sandbox database
A blank Neo4j Sandbox instance has been created for you to use during this course.
You can open a Neo4j Browser window throughout this course by clicking the 
button in the bottom right-hand corner of the screen.
What is Neo4j Sandbox?
Neo4j Sandbox is a free service that allows you to create pre-populated Neo4j instances.
Neo4j Sandbox is the perfect environment for experimenting with Neo4j.
You can log into Neo4j Sandbox and create a database with many pre-populated datasets by visiting sandbox.neo4j.com.
Extending Your Sandbox Instance
By default, a Neo4j sandbox instance exists for 3 days. You can extend it for another 7 days by going to the sandbox site and extending it in the details (right-most down arrow) for the blank sandbox.
Click Ready when you are ready to get started.
Lesson Summary
You are ready to get started with the course.