GraphAcademy

Building Knowledge Graphs with LLMs

Knowledge graphs

LLM Graph Builder

Build a Graph with Python

Querying Knowledge Graphs

Setup your development environment

In this module, you will use Python, LangChain, and OpenAI to create a knowledge 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 - github.com/neo4j-graphacademy/llm-knowledge-graph-construction. It contains the starter code and resources you need.

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.

Open Gitpod workspace →

You will need to login to Gitpod with a Github account.

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-knowledge-graph-construction repository:

bash
git clone https://github.com/neo4j-graphacademy/llm-knowledge-graph-construction

Install the required packages using pip and download the required data:

bash
cd llm-knowledge-graph
pip install -r requirements.txt

You 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.

Create a .env file
OPENAI_API_KEY=
NEO4J_URI=
NEO4J_USERNAME=
NEO4J_PASSWORD=

Add your Open AI API key (OPENAI_API_KEY), which you can get from platform.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-knowledge_graph/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.