resume

resume

This is a sample notebook and web application which shows how Google Vertex AI Generative AI can be used with Neo4j. We will explore how to leverage Google VertexAI LLMs to build and consume a knowledge graph in Neo4j.

This notebook parses data from a public corpus of Resumes / Curriculum Vitae using Google Vertex AI Generative AI's text-bison model. The model is prompted to recognise and extract entities and relationships.

We then use the code-bison model from the Codey family and prompt it to convert questions in English to Cypher - Neo4j's query language for data retrieval.

Setup

To get started, create a managed notebook in Google Cloud Vertex AI.

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-google-cloud.git

The notebook uses Python 3.8 for LangChain. However the managed notebooks are currently on 3.7. So, we'll need to install a newer version of Python. You can do that by running these commands in the terminal.

VENV=py38
conda create -y -q -p $HOME/conda_env/$VENV python=3.8 ipykernel
source /opt/conda/bin/activate ~/conda_env/$VENV
python -m ipykernel install --user --name $VENV
conda install -y -c conda-forge ipywidgets

Notebook

The notebook at notebook/notebook.ipynb walks through prompts and tuning a model. You will need to run that before the UI. While running the notebook, make sure you select the py38 kernel you just created above. The notebook has an embeeded Gradio widget that can be run for quick testing.

UI

The UI application is based on Streamlit. In this example we're going to show how to run it on a Google Compute Engine (GCE) VM. First, deploy a VM. You need to replace your environment specific values int he command below:

export VM_INSTANCE_NAME='neo4j-gcp-genai-demo'
export GCP_PROJECT_NAME=$(gcloud config get-value project)
gcloud compute instances create $VM_INSTANCE_NAME \
    --image-project debian-cloud \
    --image-family debian-12 \
    --zone="us-central1-a"

Next, login to the new VM instance:

gcloud compute ssh --zone "us-central1-a" $VM_INSTANCE_NAME --project $GCP_PROJECT_NAME

We're going to be running the application on port 80. That requires root access, so first:

sudo su

Then you'll need to install git and clone this repo:

apt install -y git
mkdir -p /app
cd /app
git clone https://github.com/neo4j-partners/neo4j-generative-ai-google-cloud.git
cd neo4j-generative-ai-google-cloud/resume

Login using GCP credentials via the gcloud cli or assign Vertex AI API access via Service Account to the Compute Engine

gcloud auth application-default login

Let's install python & pip first:

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 GCP and Neo4j credentials. The file has the following variables:

GCP_PROJECT = "myprojectname" # Your GCP project ID
GCP_LOCATION = "us-central1" # Location
TUNED_CYPHER_MODEL = "" # If you have a tuned Codey Model, provide here. Else, Leave it blank
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:

cd ..
streamlit run main.py --server.port=80

On a GCP VM to run on port 80:

• Ensure you are a root or has access to run on port 80

• If you are running sudo, you also need to run the gcloud auth command above as a sudoer. And ensure that streamlit is accessible from the PATH.

• Ensure that the VM has port 80 open for HTTP access. You might need to open that port or any other via firewall-rules. You can use the following gcloud command to open the port. Make sure you replace with relevant values. You also need to add network tags to your VM before executing this command:

  gcloud compute firewall-rules create <rule-name> --allow tcp:80 --source-tags=<list-of-your-instances-name-tags> --source-ranges=0.0.0.0/0 --description="<your-description-here>"

From the UI, you can ask questions like:

1. How many experts do we have on Google Docs?
2. What skills does p06150 have?
3. What are all the companies did p06150 work for?
4. What skills do p06150 and p10343 have in common?
5. Who went to most number of universities and how many did they go to?
6. How many pythonistas are there?