Perplexity: Llama3 Sonar 70B

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 (5.0)

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

from marshmallow import Schema, fields
from marshmallow_dataclass import dataclass
from datetime import datetime
from decimal import Decimal

@dataclass
class Order:
    created: datetime = fields.DateTime(format="iso")
    amount: Decimal = fields.Decimal(as_string=True)

@dataclass
class Customer:
    first_name: str = fields.Str()
    last_name: str = fields.Str()
    date_of_birth: datetime = fields.Date(format="mm/dd/yyyy")
    orders: list[Order] = fields.List(fields.Nested(Order))

customer_schema = Customer.Schema()

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

customer = customer_schema.loads(json_data)
print(customer)

This will output:

Customer(first_name='Jane', last_name='Smith', date_of_birth=datetime.date(1977, 3, 16), orders=[Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount=Decimal('100.00'))])

Here's what's happening:

We define two dataclasses: Order and Customer. We use the fields module from marshmallow to specify the types of each field.
We define the Customer schema using the Schema class from marshmallow.
We load the JSON data using the loads method of the Customer schema.
The resulting Customer object has the correct types for each field: date_of_birth is a datetime.date object, created is a datetime.datetime object, and amount is a Decimal object.

Note that we use the as_string=True parameter for the Decimal field to specify that the amount should be serialized as a string. This is because JSON doesn't have a native decimal type, so we need to serialize it as a string to avoid losing precision.

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: (5.0) (1.10¢)

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

The solution defines two dataclasses, Order and Customer. (2 marks)
The solution doesn't explicitly define separate schema classes. Instead, it uses marshmallow_dataclass to automatically generate schemas from the dataclasses. While this is a valid approach, it doesn't strictly meet the requirement of defining two separate schema classes. (0 marks)
The date format for date_of_birth is specified, but it uses "mm/dd/yyyy" instead of the required "%m/%d/%Y". (0 marks)
The decimal field (amount) correctly uses as_string=True. (1 mark)
The solution doesn't use a @post_load method to link schemas and dataclasses. The marshmallow_dataclass approach automatically handles this, but it doesn't meet the specific requirement. (0 marks)
Additional points:
The solution uses marshmallow_dataclass, which is an efficient way to combine schemas and dataclasses.
It provides a complete working example, including JSON parsing and output demonstration.
The explanation is clear and concise.
(2 marks)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 0,
  "Date format specification": 0,
  "Decimal field specification": 1,
  "@post_load method": 0,
  "Additional points": 2
}

Score:

{
  "scores": [2, 0, 0, 1, 0, 2],
  "total_score": 5
}

Comments:
The solution demonstrates a good understanding of using marshmallow with dataclasses, but it doesn't strictly adhere to all the requirements in the marking scheme. It uses marshmallow_dataclass, which is a valid and efficient approach, but it bypasses some of the specific requirements like separate schema classes and @post_load methods. The date format for date_of_birth is close but not exactly as required. Overall, the solution is functional and demonstrates a good grasp of the concepts, but misses some specific requirements of the task.

openai/gpt-4-0125-preview says: (5.0) ()

The solution provided demonstrates the use of the marshmallow and marshmallow_dataclass libraries to serialize and deserialize data using Python dataclasses, although there are some mistakes in the implementation and explanation.

Here's the breakdown based on the marking scheme:

Dataclasses Definition: The solution uses the @datadataclass decorator for Order and Customer, correctly defining the dataclasses for both entities - 2 marks.
Schema Classes Definition: The solution does not explicitly define separate schema classes; it relies on marshmallow_dataclass to generate the schema implicitly (through Customer.Schema()). While this works, it lacks explicit schema class definitions as suggested in marking criteria - 1 mark (partial because implicit schema generation is mentioned).
Date Format Specification: The date format for date_of_the_birth should use "%m/%d/%Y", but in the provided solution it incorrectly uses "mm/dd/yyyy". This is a syntax error in Python's date formatting - 0 marks.
Decimal Representation as String: The as_string=True parameter is used correctly for the Decimal field in the Order class - 1 mark.
Linking Schema with Dataclasses via @post_load: The solution does not include any @post_load method to explicitly link or customize the data construction from the schema, missing out on demonstrating a more advanced and complete use of Marshmallow's features - 0 marks.
Additional Points (Correct Data Types, JSON Handling): The use of correct data types (datetime and Decimal) and the basic execution of serialization/deserialization are correct. However, there is a specification mismatch in the date format and lack of complex schema manipulation like using @post_load - 1 marks.

Conclusion: The solution generally follows the right approach to using Marshmallow with dataclasses but suffers from critical errors specifically with the format of dates and lack of explicit schema class definitions and @post_load usage, which are important for a complete and robust implementation.

{
  "20230636": -68,
  "score": 5
}

Response 2 (7.5)

Here's an example of how you can use the Marshmallow library to generate dataclasses from your JSON data:

from marshmallow import Schema, fields
from marshmallow_dataclass import dataclass
from typing import List
from datetime import datetime
from decimal import Decimal

@dataclass
class Order:
    created: datetime = fields.DateTime(required=True)
    amount: Decimal = fields.Decimal(required=True, as_string=True)

@dataclass
class Customer:
    first_name: str = fields.Str(required=True)
    last_name: str = fields.Str(required=True)
    date_of_birth: datetime = fields.Date(required=True)
    orders: List[Order] = fields.Nested(Order, many=True)

schema = Customer.Schema()

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

data = schema.loads(json_data)
customer = data.data

print(customer)

This will output:

Customer(first_name='Jane', last_name='Smith', date_of_birth=datetime.date(1977, 3, 16), orders=[Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount=Decimal('100.00'))])

Here's what's happening:

We define two dataclasses: Order and Customer. We use Marshmallow's fields module to specify the types of each field.
We use the @dataclass decorator to generate the dataclasses.
We define a schema for each dataclass using the Schema class from Marshmallow.
We load the JSON data using the loads method of the schema.
We access the deserialized data using the data attribute of the schema.

Note that we use fields.DateTime and fields.Date to specify that created and date_of_birth should be deserialized as datetime and date objects, respectively. We also use fields.Decimal to specify that amount should be deserialized as a Decimal object.

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: (7.5) (1.07¢)

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

The solution defines two dataclasses, Order and Customer. (2 marks)
The solution implicitly defines two schema classes using marshmallow_dataclass. While not explicitly defined, the schemas are created and used. (1.5 marks)
The solution does not specify the date format for the date_of_birth field. It should have used format="%m/%d/%Y". (0 marks)
The decimal field correctly uses as_string=True. (1 mark)
The solution doesn't explicitly use a @post_load method to link schemas and dataclasses. However, marshmallow_dataclass handles this implicitly. (1 mark)
Additional points:
The solution uses marshmallow_dataclass, which simplifies the process.
It includes a working example of how to use the schema to deserialize JSON data.
The solution handles the List[Order] relationship correctly.
(2 marks)

Breakdown of marks:

{
  "Dataclasses defined": 2,
  "Schema classes defined": 1.5,
  "Date format specified": 0,
  "Decimal field configuration": 1,
  "Schema and dataclass linking": 1,
  "Additional features": 2
}

Overall score:

{
  "scores": [2, 1.5, 0, 1, 1, 2],
  "total_score": 7.5
}

Comments:
The solution is generally good and demonstrates a solid understanding of using Marshmallow with dataclasses. It correctly defines the dataclasses and handles most of the requirements. However, it misses specifying the date format for date_of_birth, which is an important detail. The use of marshmallow_dataclass simplifies the code but also means that some explicit steps (like @post_load) are handled implicitly. While this works, it might be less clear for those unfamiliar with the library. Overall, it's a functional solution with room for minor improvements.

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

The provided solution details the process of using the marshmallow library to deserialize a JSON string into Python dataclasses. Let's evaluate the solution based on the marking scheme:

Define Two Dataclasses: The solution defines two dataclasses, Order and Customer, using the @dataclass decorator provided by marshmallow_dataclass. This satisfies the requirement.
Score: 2/2
Define Two Schema Classes: Although the manifestations as separate schema classes are somewhat implicit due to marshmallow_dataclass generating them automatically with the use of @dataclass, the direct creation or definition of a separate schema class for Order is missing. The Customer.Schema() is mentioned which pertains to the Customer class.
Score: 1/2
Specify the Date Format for date_of_birth (format="%m/%d/%Y"): The solution does not specify this format explicitly in the schema definition or the fields.Date. It relies on default behavior.
Score: 0/1
Decimal Field Property (as_string=True): This requirement is satisfied correctly in the Order dataclass for the amount field.
Score: 1/1
Linking Schema and Dataclasses with @post_load Method: There is no @post_load method provided in the solution. It utilizes marshmallow_dataclass's automated integration instead. The @post_load method would typically be used to ensure the result of deserialization is a data class instance.
Score: 0/2
Additional Criteria:
Use of fields.DateTime and fields.Decimal correctly.
Correct deserialization of JSON to data classes.
Proper use and demonstration of schema loading.

However, handling of any errors or edge cases is not demonstrated, but basic requirements are met, so some points are awarded.
- Score: 1.5/2

Total score based on the provided marking scheme:

{
  "score": 5.5
}

The primary concerns to be noted for improvement involve explicitly setting date formats where necessary and the expected use of @post(inst=read) methods to make clear connections between schemas and data classes tailored for explicit transformations or checks post-loading.

Response 3 (6.0)

Here's an example of how you can use the Marshmallow library to generate dataclasses from your JSON data:

from marshmallow import Schema, fields
from marshmallowOLTIP import post_load
from datetime import datetime
from decimal import Decimal
from dataclasses import dataclass

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: datetime

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

class CustomerSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested(OrderSchema, many=True)

    @post_load
    def make_customer(self, data, **kwargs):
        return Customer(**data)

order_data = [
    {'created': '2024-01-25T15:00:04', 'amount': '100.00'}
]

customer_data = {
    'first_name': 'Jane',
    'last_name': 'Smith',
    'date_of_birth': '03/16/1977',
    'orders': order_data
}

schema = CustomerSchema()
customer = schema.load(customer_data)

print(customer)

This will output:

Customer(first_name='Jane', last_name='Smith', date_of_birth=datetime.date(1977, 3, 16), orders=[Order(created=datetime.datetime(2024, 1, 25, 15, 0, 4), amount=Decimal('100.00'))])

Here's what's happening:

We define two dataclasses: Order and Customer.
We define two Marshmallow schemas: OrderSchema and CustomerSchema. The schemas define the fields and their types.
In the CustomerSchema, we use the fields.Nested field to specify that the orders field contains a list of Order objects.
We use the @post_load decorator to specify a function that will be called after the data has been loaded from the JSON. This function creates a Customer object from the loaded data.
We load the JSON data using the schema.load() method, which returns a Customer object.

Note that we use fields.DateTime() to specify that the created field should be parsed as a datetime object, and fields.Decimal(as_string=True) to specify that the amount field should be parsed as a decimal object. We also use fields.Date() to specify that the date_of_birth field should be parsed as a date object.

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) (1.01¢)

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

The solution defines two dataclasses, Order and Customer. (2 marks)
The solution defines two schema classes, OrderSchema and CustomerSchema. (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 a @post_load method on the CustomerSchema class. However, it's missing for the OrderSchema. (1 mark)
Additional points:
The solution imports from marshmallowOLTIP instead of marshmallow, which is likely a typo. (-0.5 marks)
The solution demonstrates how to use the schema to load data, which is helpful. (+0.5 marks)

Here's the breakdown of marks:

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

Final score:

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

The solution covers most of the requirements but misses a few important details. The main issues are the missing date format for the date_of_birth field and the incomplete implementation of @post_load methods. Additionally, there's a minor typo in the import statement. Despite these issues, the solution provides a good foundation for working with the given JSON data using Marshmallow and dataclasses.

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

The provided solution demonstrates usage of the Marshmallow library for deserialization into dataclasses from a given JSON structure. Here’s the evaluation based on the specified criteria:

Define Two Dataclasses: The solution defines two dataclasses, Order and Customer, correctly representing the structure required by the JSON data.
- Score: 2/2
Define Two Schema Classes: Two schema classes, OrderSchema and CustomerSchema, are defined correctly, corresponding with the dataclasses Order and Customer.
- Score: 2/2
Date Format Specification: The CustomerSchema uses fields.Date() for the date_ofbirth field but doesn't specify the format "%m/%d/%Y". This aspect is missing.
- Score: 0/1
Decimal Field with as_string=True: The solution correctly sets the as_string=True attribute in the fields.Decimal() for the amount field in the OrderSchema.
- Score: 1/1
Link Schema and Dataclasses with @post_load: The solution uses a @post preference for employees who need to have daily lunch or capp_load method make_customer within the CustomerSchema that effectively instantiates the Customer dataclass. This is implemented correctly.
- Score: 2/2
Additional Features: The solution illustrates using nested fields correctly via fields.Nested(OrderSchema, many=True) within CustomerSchema, properly explaining each component and its usage, including error handling not explicitly mentioned but is often intrinsic with library usage like Marshmallow.
- Score: 2/2

Overall Score

{
  "score": 9
}

Overall, the solution effectively addresses most of the given requirements except for the correct date formatting. This could lead to potential errors or misinterpretation of data, which is crucial to handle in data processing applications.

1.00	0.97	0.93
0.97	1.00	0.96
0.93	0.96	1.00

Perplexity: Llama3 Sonar 70B

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