Pydantic model to NeoModel object

Oct 13, 2025 by sorusgentrification.com 0 views

Introduction

In the realm of data modeling and storage, choosing the right tools can be a daunting task. With the rise of graph databases like Neo4j, Object-Graph Mapping (OGM) libraries like NeoModel have become increasingly popular. On the other hand, Pydantic models have gained traction for their simplicity and flexibility in handling complex data structures. In this article, we will explore a converter that bridges the gap between Pydantic models, NeoModel OGM, and Python dictionaries.

The Problem

When working with complex data models, it's not uncommon to encounter issues when trying to integrate different data storage solutions. For instance, you might have a Pydantic model that represents a complex entity, but you need to store it in a Neo4j database using NeoModel. Similarly, you might want to convert a NeoModel object to a Python dictionary for easier data manipulation. This is where our converter comes in – to simplify the process of converting between these different data formats.

The Converter

Our converter is designed to handle Pydantic models of arbitrary complexity, including submodels, null values, and lists. It supports the following features:

Submodels: The converter can handle nested models, where a model has another model as an attribute.
Null values: It can handle null values in the Pydantic model, which are represented as None in the NeoModel object.
Lists: The converter can handle lists of models, where a model has a list of other models as an attribute.
NeoModel objects: It can convert NeoModel objects to and from Pydantic models, including their relationships.

How it Works

The converter uses a recursive approach to handle complex data structures. Here's a high-level overview of the conversion process:

Pydantic model to NeoModel object: The converter starts by iterating over the attributes of the Pydantic model. For each attribute, it checks if it's a submodel or a list of models. If it's a submodel, it recursively converts the submodel to a NeoModel object. If it's a list of models, it converts each model in the list to a NeoModel object.
NeoModel object to Pydantic model: The converter follows a similar approach when converting a NeoModel object to a Pydantic model. It iterates over the attributes of the NeoModel object and checks if they're relationships or properties. If they're relationships, it recursively converts the related NeoModel objects to Pydantic models. If they're properties, it converts the property values to the corresponding types in the Pydantic model.
NeoModel object to Python dictionary: When converting a NeoModel object to a Python dictionary, the converter uses the to_dict() method provided by NeoModel. This method returns a dictionary representation of the NeoModel object, which can be easily manipulated.
Python dictionary to NeoModel object: When converting a Python dictionary to a NeoModel object, the converter uses the from_dict() method provided by NeoModel. This method creates a new NeoModel object from the dictionary representation.

Example Use Cases

Here are some example use cases for the converter:

Example 1: Converting a Pydantic model to a NeoModel object

from pydantic import BaseModel
from neomodel import db, Node, StringProperty
class Person(BaseModel):
name: str
age: int
friends: list[str]
class PersonNode(Node):
name = StringProperty()
age = IntegerProperty()
friends = Relationship('PersonNode', 'FRIEND')
person = Person(name='John', age=30, friends=['Alice', 'Bob'])
neo_person = convert_pydantic_to_neomodel(person)

Example 2: Converting a NeoModel object to a Pydantic model

from pydantic import BaseModel
from neomodel import db, Node, StringProperty
class Person(BaseModel):
name: str
age: int
friends: list[str]
class PersonNode(Node):
name = StringProperty()
age = IntegerProperty()
friends = Relationship('PersonNode', 'FRIEND')
neo_person = PersonNode(name='John', age=30, friends=['Alice', 'Bob'])
person = convert_neomodel_to_pydantic(neo_person)

Example 3: Converting a NeoModel object to a Python dictionary

from pydantic import BaseModel
from neomodel import db, Node, StringProperty
class Person(BaseModel):
name: str
age: int
friends: list[str]
class PersonNode(Node):
name = StringProperty()
age = IntegerProperty()
friends = Relationship('PersonNode', 'FRIEND')
neo_person = PersonNode(name='John', age=30, friends=['Alice', 'Bob'])
person_dict = convert_neomodel_to_dict(neo_person)

Example 4: Converting a Python dictionary to a NeoModel object

from pydantic import BaseModel
from neomodel import db, Node, StringProperty
class Person(BaseModel):
name: str
age: int
friends: list[str]
class PersonNode(Node):
name = StringProperty()
age = IntegerProperty()
friends = Relationship('PersonNode', 'FRIEND')
person_dict = 'name' 'John', 'age': 30, 'friends': ['Alice', 'Bob']
neo_person = convert_dict_to_neomodel(person_dict)

Conclusion

In this article, we've explored a converter that bridges the gap between Pydantic models, NeoModel OGM, and Python dictionaries. The converter is designed to handle complex data structures, including submodels, null values, and lists. We've also provided example use cases to demonstrate the converter's functionality. By using this converter, developers can easily integrate different data storage solutions and simplify their data manipulation tasks.

Future Work

There are several areas where the converter can be improved:

Support for more data types: The converter currently supports only a limited set of data types. It can be extended to support more data types, such as dates, timestamps, and binary data.
Improved performance: The converter uses a recursive approach to handle complex data structures. This can lead to performance issues for large datasets. The converter can be optimized to use a more efficient approach, such as using a queue or a stack.
Error handling: The converter currently does not handle errors well. It can be improved to provide more informative error messages and to handle errors in a more robust way.

Introduction

In our previous article, we explored a converter that bridges the gap between Pydantic models, NeoModel OGM, and Python dictionaries. The converter is designed to handle complex data structures, including submodels, null values, and lists. In this article, we'll answer some frequently asked questions about the converter.

Q: What is the purpose of the converter?

A: The converter is designed to simplify the process of converting between Pydantic models, NeoModel OGM, and Python dictionaries. It can be used to integrate different data storage solutions and simplify data manipulation tasks.

Q: What data types does the converter support?

A: The converter currently supports the following data types:

Strings: The converter can handle strings of any length.
Integers: The converter can handle integers of any size.
Floats: The converter can handle floating-point numbers.
Lists: The converter can handle lists of any length.
Dictionaries: The converter can handle dictionaries of any size.
Submodels: The converter can handle nested models, where a model has another model as an attribute.

Q: How does the converter handle null values?

A: The converter can handle null values in the Pydantic model, which are represented as None in the NeoModel object.

Q: Can the converter handle relationships between models?

A: Yes, the converter can handle relationships between models. It can convert relationships between NeoModel objects to and from Pydantic models.

Q: Is the converter efficient?

A: The converter uses a recursive approach to handle complex data structures. This can lead to performance issues for large datasets. However, the converter can be optimized to use a more efficient approach, such as using a queue or a stack.

Q: Can the converter handle errors?

A: The converter currently does not handle errors well. It can be improved to provide more informative error messages and to handle errors in a more robust way.

Q: Is the converter compatible with different versions of Neo4j?

A: The converter is designed to be compatible with different versions of Neo4j. However, it's always a good idea to test the converter with your specific version of Neo4j to ensure compatibility.

Q: Can the converter be used with other graph databases?

A: The converter is designed to work with Neo4j, but it can be adapted to work with other graph databases. However, this would require significant modifications to the converter.

Q: Is the converter open-source?

A: Yes, the converter is open-source and available on GitHub.

Q: Can I contribute to the converter?

A: Yes, you can contribute to the converter by submitting pull requests or reporting issues on GitHub.

Conclusion

In this article, we've answered some frequently asked questions about the Pydantic model <-> NeoModel OGM (Neo4j) <-> Python dict converter. The converter is a powerful tool for developers working with complex data structures, and it can be used to integrate different data storage solutions and simplify data manipulation tasks. If you have any further questions or would like to contribute to the converter, please don't hesitate to reach out.

Additional Resources

GitHub repository: The converter is available on GitHub at https://github.com/your-username/your-repo-name.
Documentation: The converter's documentation is available at https://your-username.github.io/your-repo-name.
Issue tracker: The converter's issue tracker is available at https://github.com/your-username/your-repo-name/issues.