Introduction to Vector Indexes and Unstructured Data
Understand and search unstructured data using vector indexes
In this 1-hour course, you will learn
This course teaches you how to use Neo4j and vector indexes to understand unstructured data.
You will explore unstructured datasets, create Full definition for embedding (opens in a new tab)Information represented as a numerical vector, positioned so that similar information sits close together. and vector indexes and use them to search and understand the data.
You will learn how to process unstructured data, chunking strategies, and create Full definition for relationship (opens in a new tab)A named, directed connection between two nodes. Every relationship has a type, a start node and an end node. between the data.
You will build a graph database of unstructured data, use Python, LangChain, and OpenAI to process the data, create embeddings, and import it into Neo4j.
After completing this course, you will have the knowledge and skill to build a graph of your unstructured data and query it using vector indexes.
Prerequisites
Before taking this course, you should have:
- A basic understanding of Graph Databases and Neo4j
- Knowledge of Python and capable of reading simple programs
We recommend taking the Neo4j Fundamentals and Cypher Fundamentals courses.
Although not essential, you may also benefit from taking the following courses to understand how to import data and interact with Neo4j using Python and Go to glossary for large language model (opens in a new tab)A model trained on text to predict the next token, and so to generate language.:
To complete the practical tasks within this course, you will need Python and OpenAI API key and billing account.
Semantic search, unstructured data, and vector indexes
How to create embeddings using LLMs and LangChain
To build a graph database of unstructured data