This is a sample notebook and web application which shows how Azure OpenAI can be used with Neo4j. We will explore how to leverage the Azure OpenAI Service to build and consume a knowledge graph in Neo4j.
This notebook parses data from a public corpus of Medical Case Sheet using the Azure OpenAI Service's gpt-4-32k model. The model is prompted to recognise and extract entities and relationships.
We then use the gpt-4-32k model and prompt it to convert questions in English to Cypher - Neo4j's query language for data retrieval.
To get started, we'll need to set up some resources:
- A Neo4j instance running on Azure cloud. You can find a video tutorial here.
- Generate an OpenAI API key.
- Create a managed notebook in Azure ML.
Once that has started, open the notebook and a terminal window within that. Clone this repo with the command:
git clone https://github.com/neo4j-partners/neo4j-generative-ai-azure.git
The notebook at ingestion/ingestion.ipynb walks through prompts and tuning a model. You will need to run that before the UI.
The UI application is based on Streamlit.
Let's install python & pip first:
sudo apt-get update
apt install -y python
apt install -y pip
Now, let's create a Virtual Environment to isolate our Python environment and activate it
apt-get install -y python3-venv
python3 -m venv /app/venv/genai
source /app/venv/genai/bin/activate
To install Streamlit and other dependencies:
cd ui
pip install -r requirements.txt
Check if streamlit command is accessible from PATH by running this command:
streamlit --version
If not, you need to add the streamlit binary to PATH variable like below:
export PATH="/app/venv/genai/bin:$PATH"
Next up you'll need to create a secrets file for the app to use. Open the file and edit it:
cd streamlit
cd .streamlit
cp secrets.toml.example secrets.toml
vi secrets.toml
You will now need to edit that file to reflect your Azure OpenAI and Neo4j credentials. The file has the following variables:
OPENAI_API_KEY = "" #OPENAI KEY
OPENAI_API_TYPE = "azure"
OPENAI_API_VERSION = "2023-03-15-preview"
OPENAI_API_BASE = ""
OPENAI_MODEL_NAME = "gpt-4-32k"
OPENAI_DEPLOYMENT_NAME = "gpt-4-32k"
NEO4J_URI = "neo4j+s://xxxxx.databases.neo4j.io" # Neo4j URL. Include port if applicable
NEO4J_USER = "neo4j" # Neo4j User Name
NEO4J_PASSWORD = "Foo12345678" #Neo4j Password
Now we can run the app with the commands:
streamlit run Home.py --server.port=80
- Which patient has the most number of symptoms?
- Which disease affect most of my patients?
- How many of my patients suffer from both cough and weight loss?
- When someone has cough and nausea, do they also lose weight?
- Which body part got affected in most of my patients?
- Which age group has the most number of heart diseases?