Neo4j Knowledge Graph Generator

This tool facilitates the creation of a knowledge graph from unstructured text from PDF documents and their subsequent storage in a Neo4j database. It is ideal for processing documents like insurance policies or annual reports.

Prerequisites

Before you begin, ensure that Python 3.x is installed on your system. You can download it from Python's official website.

Setting Up PDF Ingestion

Prepare your PDFs

Ensure your PDF files are formatted correctly for ingestion. Update the main.py file with a list of documents as shown in the sample structure:

{
    "url": "PDF URL https://...",
    "title": "Title of the pdf",
    "context": "Insurance Document or Annual Report"
}

Install Dependencies

Install the required Python dependencies:

pip install -r requirements.txt

Configure Environment Variables

Set up the necessary environment variables:

1. Copy the sample environment configuration file:

cp .env.example .env

2. Fill in the details in .env for OpenAI and your Neo4j database.

Start the Ingestion Process

Launch the application to begin importing your PDFs into Neo4j:

python main.py

Launching the User Interface

Run the Streamlit Application

To start the Streamlit-based user interface, use the following command from the root of the repository:

streamlit run ./ui/chatbot.py

This command initializes the UI, allowing you to interact with the extracted text data through a convenient web interface.