Perplexity: Llama3 Sonar 8B

Description

Llama3 Sonar is Perplexity's latest model family. It surpasses their earlier Sonar models in cost-efficiency, speed, and performance.

This is a normal offline LLM, but the online version of this model has Internet access.

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)

You can use the marshmallow library to generate the dataclasses. Here's an example of how you can do it:

from datetime import datetime
from marshmallow import Schema, fields, post_load
from marshmallow import ValidationError

class OrderSchema(Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(as_string=True)

    @post_load
    def deserialize_amount(self, data):
        try:
            return {'amount': decimal.Decimal(data['amount'])}
        except ValueError:
            raise ValidationError('Invalid amount')

class UserSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.Nested(OrderSchema, many=True)

@post_load
def deserialize_orders(self, data):
    if 'orders' in data:
        data['orders'] = [OrderSchema().load(order) for order in data['orders']]
    return data

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

user = user_schema.load(user_data)

print(user)

In this example, we define two schemas: OrderSchema and UserSchema. OrderSchema has fields for created (a datetime field) and amount (a decimal field). UserSchema has fields for first_name, last_name, date_of_birth (a date field), and orders (a field that contains a list of Order objects).

The post_load decorator is used to perform additional deserialization logic after the data has been loaded. In this case, we use it to deserialize the amount field in OrderSchema and to deserialize the orders field in UserSchema.

Finally, we create an instance of UserSchema and use it to load the user_data dictionary. The resulting user object is a Python object that represents the data in the JSON.

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) (1.01¢)

Let's evaluate this solution based on the provided marking scheme:

The solution does not explicitly define dataclasses for the person and the order. It only defines schemas. (0 marks)
The solution correctly defines two schema classes: OrderSchema and UserSchema. (2 marks)
The solution correctly specifies the date format for the date_of_birth field using format='%m/%d/%Y'. (1 mark)
The decimal field amount is correctly defined with as_string=True. (1 mark)
The solution uses @post_load decorators for both schemas, which is correct. However, the @post_load decorator for UserSchema is not properly indented and would cause a syntax error. (1 mark)
Additional observations:
The solution imports necessary modules.
It handles potential ValueError for amount deserialization.
It provides an example of how to use the schemas.
(2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 0,
  "Schema classes definition": 2,
  "Date format specification": 1,
  "Decimal field configuration": 1,
  "Post_load methods": 1,
  "Additional features": 2
}

Score:

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

Comments:
The solution provides a good implementation using marshmallow schemas, but it misses the requirement of creating dataclasses. Instead, it relies solely on the schema classes for data representation. The @post_load decorator for UserSchema is incorrectly placed outside of the class definition, which would cause a syntax error. Despite these issues, the solution demonstrates a good understanding of marshmallow's features and correctly handles date formats and decimal fields. To improve, the solution should incorporate dataclasses and fix the indentation of the UserSchema's post_load method.

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

The provided solution has several important elements but also misses key aspects needed to fully realize the goal as described in the task:

Dataclasses Definition:
The solution does not define any dataclasses for the person or the orders. This omission means there's no structured representation for the deserialized data beyond basic dictionaries or equivalent containers that Marshmallow uses by default.
Score: 0/2
Schema Classes Definition:
Two schema classes, OrderSchema and UserSchema, are correctly defined. This captures the structure of the JSON and the relationships between 'User' and 'Orders'.
Score: 2/2
Date Format Specification:
The date_of_birth field in UserSchema correctly uses fields.Date with the format specified as '%m/%d/%Y', matching the desired conversion from string to date.
Score: 1/1
Decimal Field as_string:
The amount in OrderSchema is handled with fields.Decimal(as_string=True), ensuring that when marshalling, the decimal is treated as a string.
Score: 1/1
Linking Schema and Dataclasses via @post_load:
Though there is an attempt at implementing a @post_load method for both schemas, the connection to actual dataclasses is non-existent since no dataclasses are defined. Additionally, the @post_load methods do not return instances of dataclasses but rather modify dictionaries, which is an incorrect implementation of this guideline.
Score: 0/2
Miscellaneous Aspects:
Handling of nested orders in UserSchema and the error handling in the @post_load decorator for the OrderSchema is a nice addition.
The schema classes correctly represent all fields as requested but fail to implement the correct usage regarding object-oriented principles (mainly lacking the definition of dataclasses).
Score: 1.5/2

Overall Evaluation

The solution overview demonstrates familiarity with Marshmallow but lacks the integration with Python's dataclasses, which was a key requirement in the task. The foundational setup of Marshmallow schema and fields is well done but missed the crucial aspect of translating these schemas into dataclass-based representations for better type safety and structure.

{
  "score": 5.5
}

Despite appropriate schema definitions and configurations, the failure to implement actual dataclasses and correctly connect post-load processes to these dataclasses significantly affects the overall score.

Response 2 (4.0)

You can use the Schema class from the marshmallow library to define a dataclass that matches your JSON data. Here's an example of how you can do it:

from marshmallow import Schema, fields, datetime, Decimal
from datetime import datetime as dt

class OrderSchema(Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(2)

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.Nested(OrderSchema, many=True)

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

person = person_schema.load(data)
print(person)

This will create two dataclasses: Order and Person. The Order class has two fields: created (a datetime field) and amount (a decimal field). The Person class has three fields: first_name and last_name (both strings), date_of_birth (a date field), and orders (a list of Order objects).

The PersonSchema class is used to load the JSON data into a Person object. The load method takes the JSON data as input and returns a Person object.

Note that the fields class is used to define the types of the fields in the dataclass. The fields.DateTime class is used to define a datetime field, and the fields.Decimal class is used to define a decimal field. The fields.Date class is used to define a date field. The fields.Nested class is used to define a nested field (in this case, a list of Order objects).

Also, note that the format parameter is used to specify the format of the datetime and date fields. In this case, we're using the format '%Y-%m-%dT%H:%M:%S' for datetime fields and '%m/%d/%Y' for date fields.

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: (4.5) ()

Here's the evaluation of the provided solution based on the marking scheme:

Dataclasses: The solution references creating two dataclasses (Order and Person). However, it does not explicitly use Python's dataclasses module to define these, and the description of "will create two dataclasses" is not exactly correct in terms of how marshmallow works. It does not create dataclasses but marshmallow schema instances that can be used to serialize and deserialize objects. Therefore, this point is not fully met. (0.5 marks)
Schema Classes: The solution correctly defines two schema classes (OrderSchema and PersonSchema). Each schema properly defines fields matching the structure of the JSON object. (2 marks)
Date Format for date_of_birth: The solution specifies the date format for the date_of_birth field using format='%m/%d/%Y', which is correct. (1 mark)
Decimal Field with as_string=True: Although the solution specifies a Decimal field for amount, it does not use the as_string=True parameter, which was required by the marking scheme. This aims to serialize decimal numbers as strings to preserve precision during JSON serialization. The requirement is not met here. (0 marks)
Linking Schema and Dataclasses with @post_load: The solution does not include any @post_loader method or similar to link the schema output to actual dataclass instances or any other type of object instantiation. This is an important part of using marshmallow schemas for more structured OOP approaches. (0 marks)
Additional Considerations: The provided discussion correctly speaks about the purpose of using marshmallow and its fields. It mentions how these schemas help in deserializing the JSON to Python objects accurately. However, it lacks clear implementation details about error handling or further processing which can be crucial in real-world applications. (1 mark)

Therefore, the final calculated score out of 10 using the provided marking scheme is:

{
  "score": 4.5
}

This reflects the completion of schema definitions and some formatting specifications, but the essential aspects of object-oriented practices with dataclasses and full utilization of field parameters are insufficiently addressed.

anthropic/claude-3.5-sonnet says: (4.0) (1.09¢)

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

Defining two dataclasses (2 marks):
The solution doesn't explicitly define dataclasses. It uses Marshmallow schemas, but doesn't create actual dataclasses.
Marks: 0/2
Defining two schema classes (2 marks):
The solution correctly defines two schema classes: OrderSchema and PersonSchema.
Marks: 2/2
Specifying date format for date_of_birth (1 mark):
The solution correctly specifies the date format for date_of_birth as '%m/%d/%Y'.
Marks: 1/1
Decimal field with as_string=True (1 mark):
The solution doesn't use as_string=True for the decimal field.
Marks: 0/1
Linking schema and dataclasses with @post_load (2 marks):
The solution doesn't use @post_load to link schemas with dataclasses.
Marks: 0/2
Anything else (2 marks):
The solution correctly uses fields.Nested for the orders field.
It demonstrates how to use the schema to load data.
Marks: 1/2

Comments:
The solution provides a good start with Marshmallow schemas, but it misses some key requirements. It doesn't create actual dataclasses, which was a main part of the task. The decimal field isn't configured with as_string=True. The @post_load decorator is missing, which would be used to create dataclass instances from the loaded data. However, the solution does show a good understanding of defining schemas and nested fields in Marshmallow.

Here's the breakdown of marks:

{
  "Defining dataclasses": 0,
  "Defining schema classes": 2,
  "Date format specification": 1,
  "Decimal field configuration": 0,
  "Linking schemas and dataclasses": 0,
  "Additional considerations": 1
}

Final score:

{
  "scores": [0, 2, 1, 0, 0, 1],
  "total_score": 4
}

Response 3 (6.0)

You can use the marshmallow library to generate dataclasses from your JSON data. Here's an example of how you can do it:

import marshmallow
from marshmallow import fields
from marshmallow import date, datetime, Decimal

# Define the dataclasses
class Order(marshmallow.Schema):
    created = fields.DateTime(format='%Y-%m-%dT%H:%M:%S')
    amount = fields.Decimal(as_string=True)

class Person(marshmallow.Schema):
    first_name = fields_STR()
    last_name = fields.Str()
    date_of_birth = fields.Date(format='%m/%d/%Y')
    orders = fields.Nested(Order, many=True)

# Load the JSON data
person = Person().load({
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': [
        {
            'created': '2024-01-25T15:00:04',
            'amount': '100.00'
        }
    ]
})

print(person)  # Output: Person(first_name='Jane', last_name='Smith', date_of_birth=datetime.date(1977, 3, 16), orders=[Order(created=datetime.datetime(2024, 1, 25, 15, 0), amount=Decimal('100.00'))])

In this example, we define two dataclasses: Order and Person. The Order dataclass has two fields: created (a datetime field) and amount (a decimal field). The Person dataclass has four fields: first_name, last_name, date_of_birth (a date field), and orders (a list of Order objects).

We then use the load method of the Person dataclass to load the JSON data into an instance of the Person dataclass. The load method takes a dictionary as input, which is used to populate the fields of the dataclass.

Note that we use the fields.Date and fields.DateTime classes to define the date_of_birth and created fields, respectively. We also use the fields.Decimal class to define the amount field, and set the as_string=True parameter to allow the field to be serialized as a string.

Finally, we print the resulting Person object to see the loaded 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)

anthropic/claude-3.5-sonnet says: (6.0) (0.99¢)

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

The solution defines two schema classes (Order and Person), but not dataclasses. (1/2 marks)
The solution correctly defines two schema classes (Order and Person). (2/2 marks)
The solution correctly specifies the date format for the date_of_birth field (format='%m/%d/%Y'). (1/1 mark)
The solution correctly uses as_string=True for the decimal field. (1/1 mark)
The solution does not implement @post_load methods to link schemas and dataclasses. (0/2 marks)
Additional points:
The solution correctly uses nested fields for the orders.
It demonstrates how to load JSON data using the schema.
There's a minor typo in fields_STR() instead of fields.Str().
(1/2 marks)

Comments:
The solution provides a good starting point for using marshmallow to handle JSON data, but it misses some key requirements. It defines schema classes instead of dataclasses, which is a significant oversight. The @post_load method is missing, which is necessary to convert the loaded data into actual dataclass instances. The typo in fields_STR() should be corrected to fields.Str(). Despite these issues, the solution correctly handles date formats, decimal fields, and nested structures.

Mark breakdown:

{
  "Dataclass definition": 1,
  "Schema definition": 2,
  "Date format specification": 1,
  "Decimal field configuration": 1,
  "@post_load implementation": 0,
  "Additional points": 1
}

Score:

{
  "scores": [1, 2, 1, 1, 0, 1],
  "total_score": 6
}

openai/gpt-4-0125-preview says: (3.0) ()

The provided solution has several issues and does not meet the requirements specified in the problem fully. Here's the detailed evaluation based on the marking scheme:

Dataclasses Definition (2 marks): The solution incorrectly defines Order and Person as subclasses of marshmallow.Schema instead of using dataclasses.dataclass. This fundamental misunderstanding costs the solution full marks in this category. (0/2)
Schema Classes Definition (2 marks): Since the solution mismatches the use of marshmallow.Schema with that of dataclasses (they used schemas instead of dataclasses directly), and the task specifically required the use of dataclasses with linked schemas, this part is also incorrect. However, schemas are technically defined, so partial credit can be considered for attempting to structure the data although inappropriately for the requirements. (0.5/2)
Date Format Specification (1 mark): The format for the date_of_birth field is correctly specified as %m/%d/%Y in fields.Date. (1/1)
Decimal Field as String (1 mark): The amount field in the Order schema is correctly set with as_string=True for the fields.Decimal. (1/1)
Post Load Method for Linking Schema and Dataclasses (2 marks): The solution does not include any @post_load methods to convert the loaded data into instances of dataclasses. This is another fundamental missing part that significantly diverges from the problem’s requirements. (0/2)
Additional Requirements (2 marks): The solution does have errors (like fields_STR which should presumably be fields.Str) and lacks explanation or additional attributes that could handle conversion or validation intricacies, especially related to linking and instantiating actual dataclass instances from schemas. Some recognition for effort in setting up a nested structure for orders in the Person schema. (0.5/2)

Overall Evaluation:

The provided solution scores poorly both in terms of accuracy of understanding marshmallow in conjunction with dataclasses and fulfilling the specific requirements of the query. Major concepts are misunderstood or incorrectly applied.

{
  "score": 3
}

1.00	0.84	0.80
0.84	1.00	0.90
0.80	0.90	1.00

Perplexity: Llama3 Sonar 8B

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Overall Evaluation

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