Neo4j Runway is a Python library that simplifies the process of migrating your relational data into a graph. It provides tools that abstract communication with OpenAI to run discovery on your data and generate a data model, as well as tools to generate ingestion code and load your data into a Neo4j instance. The examples here will demonstrate these capabilities.
- Data Discovery: Harness OpenAI LLMs to provide valuable insights from your data
- Graph Data Modeling: Utilize OpenAI and the Instructor Python library to create valid graph data models
- Code Generation: Generate ingestion code for your preferred method of loading data
- Data Ingestion: Load your data using Runway's built in implementation of PyIngest - Neo4j's popular ingestion tool
Runway uses graphviz to visualize data models. To enjoy this feature please download graphviz.
You'll need a Neo4j instance to fully utilize Runway. Start up a free cloud hosted Aura instance or download the Neo4j Desktop app.
It's recommended to run these examples in a virtual environment. To install run the following from the command line in the project root directory.
pip install -r requirements.txt
Walkthrough each of the 4 key features with country data. This will cover discovery and data modeling with an LLM, ingestion code generation and ingestion into a Neo4j graph.
Learn how to create a model in arrows.app, then import it into Neo4j Runway to generate code and ingest into a graph.
This is an Streamlit implementation of Neo4j Runway that guides the user through each key feature using their own data.