neo4j-runway 0.9.1

A Python library that contains tools for data discovery, data model generation and ingestion for the Neo4j graph database.

Neo4j Runway

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.

Key Features

• 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

Requirements

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.

Get Running in Minutes

pip install neo4j-runway

Now let's walk through a basic example.

Here we import the modules we'll be using.

import pandas as pd

from neo4j_runway import Discovery, GraphDataModeler, PyIngest, UserInput
from neo4j_runway.code_generation import PyIngestConfigGenerator
from neo4j_runway.llm.openai import OpenAIDiscoveryLLM, OpenAIDataModelingLLM

Discovery

Now we...

• Define a general description of our data
• Provide brief descriptions of the columns of interest
• Provide any use cases we'd like our data model to address
• Load the data with Pandas

USER_GENERATED_INPUT = UserInput(general_description='This is data on different countries.',
    column_descriptions={
        'id': 'unique id for a country.',
        'name': 'the country name.',
        'phone_code': 'country area code.',
        'capital': 'the capital of the country.',
        'currency_name': "name of the country's currency.",
        'region': 'primary region of the country.',
        'subregion': 'subregion location of the country.',
        'timezones': 'timezones contained within the country borders.',
        'latitude': 'the latitude coordinate of the country center.',
        'longitude': 'the longitude coordinate of the country center.'
    },
    use_cases=[
        "Which region contains the most subregions?", 
        "What currencies are most popular?", 
        "Which countries share timezones?"
    ]
)

data = pd.read_csv("data/csv/countries.csv")

We then initialize our discovery llm. By default we use GPT-4o and define our OpenAI API key in an environment variable.

disc_llm = OpenAIDiscoveryLLM()

And we run discovery on our data.

disc = Discovery(llm=disc_llm, user_input=USER_GENERATED_INPUT, data=data)
disc.run()

Data Modeling

We can now pass our Discovery object to a GraphDataModeler to generate our initial data model. A Discovery object isn't required here, but it provides rich context to the LLM to achieve the best results.

modeling_llm = OpenAIDataModelingLLM()

gdm = GraphDataModeler(llm=modeling_llm, discovery=disc)
gdm.create_initial_model()

If we have graphviz installed, we can take a look at our model.

gdm.current_model.visualize()

Let's make some corrections to our model and view the results.

gdm.iterate_model(user_corrections="""
Make Region node have a HAS_SUBREGION relationship with Subregion node. 
Remove The relationship between Country and Region.
""")
gdm.current_model.visualize()

Code Generation

We can now use our data model to generate some ingestion code.

gen = PyIngestConfigGenerator(data_model=gdm.current_model, 
                         username="neo4j", password="password", 
                         uri="bolt://localhost:7687", database="neo4j", 
                         csv_dir="data/csv/", csv_name="countries.csv")

pyingest_yaml = gen.generate_config_string()

Ingestion

We will use the generated PyIngest yaml config to ingest our CSV into our Neo4j instance.

PyIngest(config=pyingest_yaml, dataframe=data)

We can also save this as a .yaml file and use with the original PyIngest.

gen.generate_config_yaml(file_name="countries.yaml")

Here's a snapshot of our new graph!

Limitations

The current project is in beta and has the following limitations:

• Single CSV input only for data model generation
• Nodes may only have a single label
• Only uniqueness and node / relationship key constraints are supported
• CSV columns that refer to the same node property are not supported in model generation
• Only OpenAI models may be used at this time
• The modified PyIngest function included with Runway only supports loading a local Pandas DataFrame or CSVs