Introduction to Vector Indexes and Unstructured Data
Course Description
This course teaches you how to use Neo4j and vector indexes to understand unstructured data.
You will explore unstructured datasets, create embeddings and vector indexes and use them to search and understand the data.
You will learn how to process unstructured data, chunking strategies, and create relationships 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:
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A basic understanding of Graph Databases and Neo4j
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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 Importing CSV data into Neo4j and Neo4j and LLM Fundamentals courses to understand how to import data and interact with Neo4j using Python and LLMs.
To complete the practical tasks within this course, you will need Python and OpenAI API key and billing account.
Duration
2 hours
What you will learn
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Semantic search, unstructured data, and vector indexes
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How to create embeddings using LLMs and LangChain
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To build a graph database of unstructured data
This course includes
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11 lessons
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8 short hands-on challenges
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10 multiple choice questions
Get Support
If you find yourself stuck at any stage then our friendly community will be happy to help. You can reach out for help on the Neo4j Community Site, or head over to the Neo4j Discord server for real-time discussions.
Feedback
If you have any comments or feedback on this course you can email us on graphacademy@neo4j.com.