OpenAI: GPT-4 32k

Description

GPT-4-32k is an extended version of GPT-4, with the same capabilities but quadrupled context length, allowing for processing up to 40 pages of text in a single pass. This is particularly beneficial for handling longer content like interacting with PDFs without an external vector database. Training data: up to Sep 2021.

Prompt

I need to take this json:

{
'first_name': 'Jane',
'last_name': 'Smith',
'date_of_birth': '03/16/1977',
orders: [
{
'created': '2024-01-25T15:00:04',
'amount': '100.00'
}
]}

And generate some dataclasses.

date_of_birth needs to be a date.
created is a datetime.
amount is a decimal.

How would I do this using the marshmallow library in python?

Response 1 (7.0)

Sure, here is how you would do it using Marshmallow. First of all, you would need to import the necessary modules and classes:

from dataclasses import dataclass
from typing import List
from datetime import date, datetime
from decimal import Decimal
from marshmallow import fields, post_load
from marshmallow_dataclass import class_schema

Then, you would define your data classes:

@dataclass
class Order:
    created: datetime
    amount: Decimal


@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: List[Order]

In these class definitions, created, amount, and date_of_birth are typed as datetime,Decimal, and date as per your specifications.

Now, you want to deserialize JSON into these dataclasses. Marshmallow dataclass can generate a schema automatically from the dataclasses:

PersonSchema = class_schema(Person)()

class_schema generates a schema which we can use to load/validate/dump data. It also handles nested fields, so the schema for 'Order' is automatically generated and included in the 'Person' schema.

Since created, amount, and date_of_birth require custom parsing, you'll need to override these fields using a marshmallow schema:

class CustomPersonSchema(PersonSchema):
    date_of_birth = fields.Date()
    orders = fields.Nested(class_schema(Order)(), many=True)


class CustomOrderSchema(class_schema(Order)()):
    created = fields.DateTime()
    amount = fields.Decimal(as_string=True)

Finally, this is how you deserialize a person from JSON:

json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '1977-03-16',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

person, errors = CustomPersonSchema().load(json_data)

In this case person is now an instance of Person, with date_of_birth correctly parsed as a date, and each order in orders is an instance of Order, with created as a datetime and amount as decimal.

Note: the as_string=True argument for the Decimal field. If False (the default), the field will deserialize to decimal.Decimal objects. If True, the field will deserialize to strings.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (7.0) (1.09¢)

I'll evaluate the solution based on the provided marking scheme:

The solution defines two dataclasses, Person and Order. (2 marks)
The solution defines two schema classes, CustomPersonSchema and CustomOrderSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field as "%m/%d/%Y". It uses the default format. (0 marks)
The decimal field correctly uses as_string=True. (1 mark)
The schema and dataclasses are not explicitly linked with a @post_load method. The solution uses marshmallow_dataclass's class_schema instead. (0 marks)
Additional points:
The solution correctly imports necessary modules and classes. (1 mark)
It uses marshmallow_dataclass to generate schemas automatically, which is an efficient approach. (1 mark)

Here's the breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 2,
  "Date format specified": 0,
  "Decimal field as_string": 1,
  "Schema-dataclass linking": 0,
  "Additional points": 2
}

The total score:

{
  "scores": [2, 2, 0, 1, 0, 2],
  "total_score": 7
}

Comments:
The solution is generally good and demonstrates a solid understanding of using Marshmallow with dataclasses. It correctly defines the dataclasses and uses marshmallow_dataclass to generate schemas, which is an efficient approach. The solution also correctly handles the decimal field with as_string=True.

However, there are a few areas for improvement:
1. The date format for date_of_birth is not specified as required.
2. The solution doesn't use the @post_load decorator to link schemas and dataclasses explicitly.
3. The date format in the example JSON doesn't match the format specified in the problem (it uses ISO format instead of MM/DD/YYYY).

Overall, the solution is functional but misses a few specific requirements from the marking scheme.

openai/gpt-4-0125-preview says: (6.0) ()

The provided solution demonstrates utilization of the Marshmallow library and dataclasses to define classes for deserializing JSON data. Let’s evaluate it according to the provided marking scheme:

Two dataclasses, one for the person and one for the order:
The solution clearly defines two dataclasses, Person and Order, using Python's @dataclass decorator.
Score: 2/2
Two schema classes, one for the person and one for the order:
While a main schema PersonSchema is generated through class_schema function, there's no explicit declaration of a separate Order schema as a distinct class. Instead, the Order schema is nested within the CustomPersonSchema. A specific CustomOrderSchema is created though it extends from class_schema(Order)(), not explicitly declared.
Score: 1.5/2
Specify the date format for the date_of_birth field:
The solution fails to specify the date format using format="%m/%d/%Y" which is fundamental for parsing the date in the specified format from JSON data. Instead, it assumes default ISO format.
Score: 0/1
Decimal field has as_string=True:
The solution correctly uses fields.Decimal(as_string=True) for parsing the 'amount' field as a string, per the requirement.
Score: 1/1
Link the schema and dataclasses with a @post_load method:
The solution does not use or illustrate a @post_load method to parse the loaded data into dataclass instances directly after loading. It inherits this capability from class_schema implicitly but does not explicitly define or illustrate it.
Score: 0/2
Additional aspects for evaluation (e.g., handling nested fields correctly, etc.):
The solution correctly handles nested fields with fields.Nested and creates a proper schema hierarchy. Usage of automatic schema generation and overriding specific fields provides a compact solution.
Score: 1.5/2

Overall, the solution constructs a basic framework using dataclasses and improves it by adding customized schema definitions using Marshmallow. However, it misses a few critical specifications such as explicit date formatting and the usage of @post_load for better clarity.

{
  "score": 6
}

Response 2 (7.0)

Here is how you would create dataclasses for the given JSON using the Marshmallow library in Python. For more details on marshmallow library, you can refer to https://marshmallow.readthedocs.io/en/stable/

Create two separate data classes, one for User and one for Order, using the marshmallow_dataclass library which is a bridge library between Marshmallow and Python's dataclasses.

from typing import List
from dataclasses import dataclass
from decimal import Decimal
from datetime import date, datetime

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class User:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: List[Order]

Next, you need to add validation and serialization/deserialization support via Marshmallow schemas. Below is an example of how to accomplish that:

import marshmallow_dataclass
from marshmallow import Schema

# The function automatically creates a marshmallow schema for your dataclass.
OrderSchema = marshmallow_dataclass.class_schema(Order)
UserSchema = marshmallow_dataclass.class_schema(User)

# Here is an example of how to use these schemas for serialization/deserialization.

user_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}

# Create an instance of UserSchema.
schema = UserSchema()

# Let's start deserialize process
deserialized_user = schema.load(user_data)

print(deserialized_user)

# Model -> Json:
serialized_data = schema.dump(deserialized_user)
print(serialized_data)

This code will load the JSON data into your User dataclass (and the nested Order dataclass inside it). If Marshmallow encounters an error while loading the data, it will raise a ValidationError.

This code will also convert your model back to JSON and it will print out the serialized data.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

openai/gpt-4-0125-preview says: (5.5) ()

Evaluating the solution based on the given criteria:

Definition of Two Dataclasses: The solution defines two dataclasses, Order and User, as requested.
Score: 2/2
Definition of Two Schema Classes: The solution uses marshmallow_dataclass.class_schema to dynamically create schema classes for Order and User.
Score: 2/2
Date Format Specification: The solution does not specify the date format for the date_of_birth explicitly as %m/%d/%Y in marshmallow. It relies on automatic dataclass schema generation without customization.
Score: 0/1
Decimal Field as String: The solution does not explicitly use as_string=True for the Decimal field. It uses the default handling by marshmallow_dataclass.
Score: 0/1
Link between Schema and Dataclasses via @post_load: The solution does not manually define Schema classes or use @post_load methods to build dataclass instances from deserialized data. Instead, it relies on automatic handling by marshmallow_dataclass.
Score: 0/2
Additional Considerations: The solution seems concise and correctly uses the library to deserialize and serialize data. The example code for serialization and deserialization works as expected using available tools. However, the customized handling aspects are missing such as formatting dates and handling decimals as strings.
Score: 1.5/2

Final score based on provided criteria:

{
  "score": 5.5
}

anthropic/claude-3.5-sonnet says: (7.0) (1.00¢)

Thank you for providing the solution. I'll evaluate it based on the marking scheme you've provided.

The solution defines two dataclasses, User and Order. (2 marks)
The solution defines two schema classes, UserSchema and OrderSchema, using marshmallow_dataclass.class_schema(). (2 marks)
The solution does not specify the date format for the date_of_birth field. It's missing the format="%m/%d/%Y" specification. (0 marks)
The decimal field (amount) is not specified with as_string=True. (0 marks)
The schema and dataclasses are automatically linked by using marshmallow_dataclass.class_schema(), which implicitly creates a @post_load method. While this achieves the desired result, it doesn't explicitly show the @post_load method as requested in the marking scheme. (1 mark)
Additional points:
The solution imports necessary libraries and types.
It provides an example of how to use the schemas for serialization and deserialization.
It handles the List[Order] relationship correctly.
(2 marks)

Here's the breakdown of the marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 0,
  "Schema-dataclass linking": 1,
  "Additional points": 2
}

And here's the total score:

{
  "scores": [2, 2, 0, 0, 1, 2],
  "total_score": 7
}

The solution demonstrates a good understanding of using dataclasses with Marshmallow, but it misses some specific requirements like date format specification and the decimal field configuration. The use of marshmallow_dataclass simplifies the process but doesn't show the explicit @post_load method. Overall, it's a functional solution that could be improved by addressing these specific points.

Response 3 (9.0)

Firstly, let's import necessary modules including datetime, dataclasses, and marshmallow. Then, create dataclasses representing your data. Lastly, you can use marshmallow_dataclass to automatically generate Marshmallow schemas from these dataclasses.

Here's what the code would look like:

from decimal import Decimal
from dataclasses import dataclass
from datetime import datetime, date
from typing import List
from marshmallow import Schema, fields, post_load

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: List[Order]

class OrderSchema(Schema):
    created = fields.DateTime(required=True)
    amount = fields.Decimal(as_string=True)

    @post_load
    def make_order(self, data, **kwargs):
        return Order(**data)

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested(OrderSchema))

    @post_load
    def make_person(self, data, **kwargs):
        return Person(**data)

# Now you can use the schemas to load/dump data
person_schema = PersonSchema()

# Let's assume we have some JSON data
json_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
}
# Deserialize the JSON data to Python dataclass
person = person_schema.load(json_data)
print(person)

The make_order and make_person methods decorated with @post_load are a part of marshmallow's deserialization process, which ensures the data is loaded into the dataclass after being deserialized.

Note: as_string=True is passed to fields.Decimal because if you were to load the value straight from JSON, you would get a float, which could cause precision issues. This way, the value is loaded as a string and converted to Decimal, keeping precision intact.

You can also use marshmallow_dataclass package to automate the process of creating schemas from dataclasses:

from decimal import Decimal
from dataclasses import dataclass
from datetime import date, datetime
from typing import List
from marshmallow_dataclass import class_schema

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Person:
    first_name: str
    last_name: str
    date_of_birth: date
    orders: List[Order]

# Generate Marshmallow Schemas from dataclasses
PersonSchema = class_schema(Person)()
OrderSchema = class_schema(Order)()

The class_schema function is a factory that generates a schema from dataclass.

Marking Scheme

The solution should define two dataclasses, one for the person and one for the order (2 marks).
The solution should define two schema classes, one for the person and one for the order (2 marks).
The solution should specify the date format for the date_of_birth field. (format="%m/%d/%Y") (1 mark)
The decimal field needs as_string=True. (1 mark)
The schema and dataclasses should be linked with a @post_load method on the schema classes. (2 marks)
Anything else: (2 marks)

anthropic/claude-3.5-sonnet says: (9.0) (0.95¢)

I'll evaluate the solution based on the provided marking scheme:

The solution defines two dataclasses, Order and Person. (2 marks)
The solution defines two schema classes, OrderSchema and PersonSchema. (2 marks)
The solution does not specify the date format for the date_of_birth field. It should have used fields.Date(format="%m/%d/%Y"). (0 marks)
The decimal field correctly uses as_string=True. (1 mark)
The schema and dataclasses are linked with @post_load methods on both schema classes. (2 marks)
Additional points:
The solution imports necessary modules.
It provides an example of how to use the schemas with sample JSON data.
It mentions an alternative approach using marshmallow_dataclass.
(2 marks)

Breakdown of marks:

{
  "Dataclasses": 2,
  "Schema classes": 2,
  "Date format": 0,
  "Decimal field": 1,
  "Post_load methods": 2,
  "Additional points": 2
}

The solution is generally good, covering most of the required elements. However, it misses specifying the date format for the date_of_birth field, which was a requirement. The alternative approach using marshmallow_dataclass is a nice addition, showing flexibility in implementation.

Score:

{
  "scores": [2, 2, 0, 1, 2, 2],
  "total_score": 9
}

openai/gpt-4-0125-preview says: (9.0) ()

The provided solution is analyzed based on the specified criteria:

Two dataclasses defined: Order and Person dataclasses are correctly defined with the specified fields and types.
Score: 2/2
Two schema classes defined: OrderSchema and PersonSchema are correctly defined.
Score: 2/2
Date format specification for date_of_birth field: The solution does not specify the date format for the date_of_birth field explicitly as %m/%d/%Y.
Score: 0/1
Decimal field as string: The solution correctly sets the decimal field (amount) within the OrderSchema to as_string=True.
Score: 1/1
@post_load method on schema classes: Both OrderSchema and PersonSchema use the @post_load method to link the schema deserialization directly to the instantiation of the dataclasses (Order and Person).
Score: 2/2
Additional considerations:
While the solution mentions using marshmallow_dataclass to automatically generate schemas from dataclasses in an additional script, the primary solution manually defines schema classes, which matches the expected detailed control over serialization behavior.
Solution is fully functional and fits the problem specifications (other than the specific date format).
Explanation of as_string=True for maintaining precision in Decimal field is clearly given.
Score: 2/2

Overall, the solution misses only on explicitly defining the date format in the schema. Apart from that small omission, it fulfills almost all the aspects outlined in the problem effectively.

Final score representation:

{
  "score": 9
}

1.00	0.84	0.91
0.84	1.00	0.79
0.91	0.79	1.00

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