GraphAcademy
Course · Part of Generative AI & GraphRAG

Introduction to Vector Indexes and Unstructured Data

Understand and search unstructured data using vector indexes

1 hour20 lessons across 3 modules
Context EngineerIntermediateProcessingGenerative AI & GraphRAGBuild a Neo4j-backed Application
About this course

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 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:

  • 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 LLMs:

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

Course content

3 modules, 1 hour.

  1. 6 lessons
    1. Getting Started5 min
    2. Semantic search5 min
    3. Finding Movie Plots5 min
    4. Vectors5 min
    5. Unstructured data5 min
    6. Searching imagesOptional5 min
  2. 5 lessons
    1. Vectors and Embeddings5 min
    2. Load embeddings5 min
    3. Create a Vector Index5 min
    4. Create the Answer Vector Index5 min
    5. Query a Vector Index5 min
  3. 9 lessons
    1. Setup your development environment5 min
    2. Structured and Unstructured Data5 min
    3. Chunking5 min
    4. Import data with Python and LangChain5 min
    5. Creating embeddings5 min
    6. Create a graph5 min
    7. Extract Topics5 min
    8. Expand the GraphOptional5 min
    9. Turning data into knowledge5 min
Where to next

Keep going on Generative AI & GraphRAG.

View the full path
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