Combining the power of Neo4j, a graph database, with large language models, such as GPT-3, can lead to fascinating applications that leverage both structured and unstructured data. In this article, we’ll explore how to integrate Neo4j with a large language model to build intelligent applications that can perform complex graph-based queries and generate natural language responses.

What is Neo4j?

Neo4j is a highly popular and efficient graph database that allows you to model, store, and query complex relationships between data points. It excels in handling interconnected data and is widely used in applications involving social networks, recommendation systems, fraud detection, and more.

Why Combine Neo4j with Large Language Models?

Large language models excel at processing unstructured text data and generating human-like responses. By integrating Neo4j with a language model like GPT-3, we can extract meaningful insights from the graph data and generate natural language responses to answer user queries.

Setting Up Neo4j and GPT-3 Integration

Before we begin, make sure you have Neo4j installed and set up. Additionally, obtain an API access key for GPT-3 to enable language processing capabilities. Once you have both ready, follow these steps:

1. Prepare Your Graph Data

In Neo4j, create or import your graph data that represents interconnected entities and relationships. For example, if you’re building a movie recommendation system, you may have nodes for movies, actors, and genres, with relationships defining which actors starred in which movies and which genres each movie belongs to.

2. Install Required Libraries

You’ll need Python and the neo4j library to interact with the Neo4j database. Install it using pip:

pip install neo4j

3. Query Neo4j Database

Write Python code to query the Neo4j database based on user input. For example, if the user wants to find movies with a specific actor, the code should retrieve relevant nodes and relationships from the graph.

from neo4j import GraphDatabase

uri = "bolt://localhost:7687"
username = "your_username"
password = "your_password"

def get_movies_with_actor(actor_name):
    with GraphDatabase.driver(uri, auth=(username, password)) as driver:
        with driver.session() as session:
            query = (
                f"MATCH (a:Actor {{name: '{actor_name}'}})-[:ACTED_IN]->(m:Movie) "
                "RETURN m.title AS movieTitle"
            result =
            movies = [record["movieTitle"] for record in result]
            return movies

4. Process the Results with GPT-3

Now that you have the query results, use GPT-3 to generate a natural language response. For this, you’ll need the openai library installed and your GPT-3 API access key configured.

pip install openai
import openai

openai.api_key = "your_gpt3_api_key"

def generate_response(user_query, movies_with_actor):
    prompt = f"Query: {user_query}\n"
    prompt += "Movies with the actor:\n"
    for movie in movies_with_actor:
        prompt += f"- {movie}\n"

    response = openai.Completion.create(

    return response["choices"][0]["text"]

5. Putting it All Together

In your Django view or any other application, call the Neo4j query function, process the results with GPT-3, and display the response to the user.

def query_movies_with_actor(request):
    actor_name = request.GET.get('actor', '')
    movies_with_actor = get_movies_with_actor(actor_name)
    response = generate_response(f"Movies with {actor_name}", movies_with_actor)

    return render(request, 'myapp/query_response.html', {'response': response})


By combining the power of Neo4j’s graph database with large language models like GPT-3, we can build intelligent applications that provide users with detailed information in a natural language format. This integration enables developers to create powerful and interactive systems that leverage structured graph data and unstructured natural language processing capabilities.

Experiment with different use cases and queries to make the most out of this powerful combination. Happy coding!