In this module, you will use Python to create a graph from unstructured data. You must set up a development environment to run the code examples and exercises.
We have created a repository for this course. It contains the starter code and resources you need.
A blank Neo4j Sandbox instance has also been created for you to use during this course.
You can open a Neo4j Browser window throughout this course by clicking the Toggle Sandbox button in the bottom right-hand corner of the screen.
Get the code
You can use Gitpod as an online IDE and workspace for this workshop. It will automatically clone the workshop repository and set up your environment.
OpenGitpod workspace →
Alternatively, you can clone the repository and set up the environment yourself.
Develop on your local machine
You will need Python installed and the ability to install packages using pip.
You may want to set up a virtual environment using venv or virtualenv to keep your dependencies separate from other projects.
Clone the github.com/neo4j-graphacademy/llm-vectors-unstructured repository:
git clone https://github.com/neo4j-graphacademy/llm-vectors-unstructuredInstall the required packages using pip and download the required data:
cd llm-vectors-unstructured
pip install -r requirements.txt
python -m textblob.download_corporaYou do not need to create a Neo4j database as you will use the provided sandbox instance.
You can find out more about how to configure Neo4j’s GenAI functions in the Neo4j GenAI integration documentation.
Setup the environment
Create a copy of the .env.example file and name it .env.
Fill in the required values.
FLASK_APP=api
FLASK_DEBUG=true
FLASK_RUN_PORT=3000
NEO4J_URI=neo4j://localhost:7687
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=neo
JWT_SECRET=secret
SALT_ROUNDS=10Add your Open AI API key (OPENAI_API_KEY), which you can get from platformn.openai.com.
Update the Neo4j sandbox connection details:
- NEO4J_URI
-
bolt://{sandbox_ip}:{sandbox_boltPort}
- NEO4J_USERNAME
-
{sandbox_username}
- NEO4J_PASSWORD
-
{sandbox_password}
Test your setup
You can test your setup by running llm-vectors-unstructured\test_environment.py - this will attempt to connect to the Neo4j sandbox and the OpenAI API.
You will see an OK message if you have set up your environment correctly. If any tests fail, check the contents of the .env file.
Continue
When you are ready, you can move on to the next task.
Summary
You have setup your environment and are ready to start this module.
In the next lesson, you will explore a strategy for storing unstructured data in a graph.