neoconverse

how to generate a schema for neoconverse…

I took a screen shot of the setup process video and then fed that into gpt-4o and this prompt “how can i generate a schema like this from a neo4j database” It worked perfectly.

To generate a schema similar to the one in the image from a Neo4j database, where nodes, properties, and relationships are displayed in a structured format, you can follow these steps:

1. Query for Node Labels and Their Properties

You can use Cypher queries to extract node labels and properties.

To list all node labels and their properties:

MATCH (n)
WITH DISTINCT labels(n) AS labels, keys(n) AS properties
UNWIND labels AS label
RETURN label, collect(DISTINCT properties) AS properties;

This will give you a list of node labels and their respective properties.

2. Query for Relationship Types

To get all relationship types and their properties:

MATCH ()-[r]->()
RETURN type(r) AS relationship, keys(r) AS properties

This query will return all relationship types and the properties associated with them.

3. Query for Specific Relationship Paths

To extract the types of relationships between nodes, use the following query:

MATCH (a)-[r]->(b)
RETURN DISTINCT labels(a), type(r), labels(b);

This will give you a list of unique relationship paths between different node labels.

4. Formatting the Output

Once you have the data from these queries, you can programmatically format the output in a style similar to what you’ve shown in the image.

For example, using Python, you can run these Cypher queries and format the result into a readable schema format.

from neo4j import GraphDatabase

def generate_schema(driver):
    schema = {}

    # Get Node Labels and Properties
    with driver.session() as session:
        node_data = session.run("MATCH (n) WITH DISTINCT labels(n) AS labels, keys(n) AS properties UNWIND labels AS label RETURN label, collect(DISTINCT properties) AS properties")
        for record in node_data:
            schema[record['label']] = record['properties']

    # Get Relationship Types
    relationships = {}
    with driver.session() as session:
        rel_data = session.run("MATCH ()-[r]->() RETURN type(r) AS relationship, keys(r) AS properties")
        for record in rel_data:
            relationships[record['relationship']] = record['properties']

    # Get Paths
    paths = []
    with driver.session() as session:
        path_data = session.run("MATCH (a)-[r]->(b) RETURN DISTINCT labels(a), type(r), labels(b)")
        for record in path_data:
            paths.append(f"({record['labels(a)']})-[:{record['type(r)']}]->({record['labels(b)']})")

    return schema, relationships, paths

# Example usage
driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))
schema, relationships, paths = generate_schema(driver)
print(schema)
print(relationships)
print(paths)

5. Manually Format to Match the Display

After running these queries and collecting the information, you can format it manually (or programmatically) in a format similar to the example in the image, with node labels and their properties first, followed by the relationships and paths.

This approach will give you a schema visualization that describes the nodes, relationships, and their properties. You can then further enhance it into a format similar to the image you uploaded, either using text or visualization libraries in Python, JavaScript, or any other tool.