Perplexity: Llama3 Sonar 70B Online

Description

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

This is the online version of the offline chat model. It is focused on delivering helpful, up-to-date, and factual responses. #online

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 (6.0)

To achieve the dataclass structure you described using the marshmallow library in Python, you can follow these steps:

Import necessary modules:

from dataclasses import dataclass
from marshmallow import Schema, fields
from marshmallow import EXCLUDE
from datetime import datetime
from decimal import Decimal

Define dataclasses:

@dataclass
class Order:
    created: datetime
    amount: Decimal

@dataclass
class Customer:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: list[Order]

Define schema classes:

class OrderSchema(Schema):
    class Meta:
        unknown = EXCLUDE
    created = fields.DateTime()
    amount = fields.Decimal()

class CustomerSchema(Schema):
    class Meta:
        unknown = EXCLUDE
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.List(fields.Nested(OrderSchema()))

Use the schema classes to serialize and deserialize:

customer = Customer(
    'Jane', 
    'Smith', 
    datetime(1977, 3, 16), 
    [
        Order(datetime(2024, 1, 25, 15, 0, 4), Decimal('100.00'))
    ]
)

schema = CustomerSchema()
data = schema.dump(customer)
print(data)

This will generate and serialize the data into JSON-compatible Python data types (lists, dictionaries, etc.).

To deserialize JSON data into these dataclasses:

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

schema = CustomerSchema()
customer = schema.load(json_data)
print(customer)

The schema defined here ensures that date_of_birth is a datetime.date, created is a datetime.datetime, and amount is a decimal.Decimal.

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) (1.02¢)

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 does not include as_string=True. It should have been fields.Decimal(as_string=True). (0 marks)
The schema and dataclasses are not linked with a @post_load method on the schema classes. (0 marks)
Additional points:
The solution imports necessary modules. (1 mark)
It demonstrates how to use the schema for serialization and deserialization. (1 mark)

Here's the breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field configuration": 0,
  "Schema and dataclass linking": 0,
  "Additional points": 2
}

Final score:

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

Comments:
The solution provides a good basic structure for using marshmallow with dataclasses. However, it misses some important details:
1. The date format for date_of_birth is not specified as required.
2. The decimal field is not configured with as_string=True.
3. There's no @post_load method to convert the deserialized data into dataclass instances.

These omissions mean that the solution won't fully meet the requirements as specified. The serialization and deserialization examples are helpful, but they would need to be adjusted to work correctly with the missing elements.

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

The provided solution has areas where it meets the requirements effectively but also contains some critical gaps according to the provided marking criteria:

Dataclass Definition for Person and Order: The solution defines dataclasses for Order (order details including datetime and decimal) and Customer (customer details including a list of Order), satisfying the criteria effectively. (+2 marks)
Schema Classes for Person and Order: The solution defines OrderSchema and CustomerSchema corresponding to the dataclasses. These container fields for created, amount, first_name, last_name, date_of_birth, and orders, which are paired with the respective dataclass types appropriately. (+2 marks)
Date Format Specification: Despite date_of_birth being required to be in the format "%m/%d/%Y", the solution does not specify this format in the CustomerSchema. This is a critical omission as it does not ensure the desired format for date handling. (+0 marks)
Decimal Field Handling: The amount field in OrderSchema should be specified with as_string=True to ensure that the decimal is serialized/deserialized as a string. This specific requirement is not met in the given solution. (+0 marks)
Linking Schema and Dataclasses with @post_load Method: The solution lacks the use of @post_load decorators in the schema classes, which is essential for transforming the loaded data back into the respective dataclass instances. This is a crucial oversight in achieving seamless serialization and deserialization of dataclass instances. (+0 marks)
Additional Criteria: The solution covers the basic serialization and deserialization techniques, handles unknown fields using EXCLUDE which helps in managing data that do not exactly match the schema, and incorporates basic usages of the Marshmallow library like fields.Nested(). Although these practices are commendable, the critical missing elements affect the overall effectiveness. (+1 mark)

Overall, while the solution demonstrates a reasonable understanding of data classes and schema definition using the Marshmallow library, it falls short in critical areas specifically related to Marshmallow features like date formatting and custom handling of decimal as strings as well as crucial linking between the schema and dataclass.

The final score, based on satisfying criteria and crucial missed elements, is:

{
  "score": 5
}

Response 2 (5.0)

Here is how you can create Python dataclasses using the marshmallow library to parse the provided JSON data:

import dataclasses
from marshmallow import Schema, fields
from marshmallow.validate import OneOf
from datetime import datetime
import decimal

@dataclasses.dataclass(frozen=True)
class Order:
    created: datetime
    amount: decimal.Decimal

@dataclasses.dataclass(frozen=True)
class Person:
    first_name: str
    last_name: str
    date_of_birth: datetime
    orders: list[Order]

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

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

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

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

# This will 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, 4), amount=Decimal('100.00'))])

This code defines two dataclasses (Order and Person), each with their respective fields. It then creates two schema classes (OrderSchema and PersonSchema) to parse the data. The fields.DateTime() is used for the created date in the order, and fields.Decimal() is used for the amount. The fields.Date() is used for the date of birth. The fields.Nested() is used to parse the list of orders. The data is loaded into the PersonSchema and then printed as a dataclass 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: (5.0) (0.97¢)

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

The solution correctly defines two dataclasses, Order and Person. (2 marks)
The solution correctly 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 does not include as_string=True. It should have been fields.Decimal(as_string=True). (0 marks)
The solution does not include @post_load methods to link the schema and dataclasses. (0 marks)
Additional observations:
The solution imports necessary modules.
It uses frozen=True in dataclasses, which is a good practice for immutability.
It includes an example usage of the schema.
(1 mark)

Here's the breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field as_string": 0,
  "Post_load method": 0,
  "Additional observations": 1
}

Final score:

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

The solution demonstrates a basic understanding of using marshmallow with dataclasses but misses some key requirements specified in the problem statement. It correctly defines the dataclasses and schema classes but fails to implement the specific date format, the as_string parameter for the decimal field, and the @post_load method to link schemas with dataclasses. These omissions significantly impact the functionality and accuracy of the data parsing process.

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

The provided solution does well in demonstrating how to use the marshmallow library with dataclasses to parse JSON data into Python objects. Here's a breakdown of the scoring:

Definition of Two Dataclasses:
Dataclasses for both Person and Order are correctly defined with appropriate types, giving this requirement full marks. (2/2)
Definition of Two Schema Classes:
Schema classes for both PersonSchema and OrderSchema are defined correctly with appropriate field definitions mapped to the corresponding dataclass fields, resulting in full marks for this criterion. (2/2)
Specification of Date Format for date_of_birth field:
The schema uses fields.Date() for date_of_birth, but does not specify the date format as "%m/%d/%Y" as required in the problem statement. Instead, this code expects the default ISO formatted date strings. (0/1)
Decimal Field with as_string=True:
The amount field uses fields.Decimal(), but does not set the as_string=True parameter, which would ensure the field is deserialized as a string retaining its exact decimal representation. This is a requirement that has been missed in the solution. (0/1)
Linking Schema and Dataclasses with @post_load Method:
The solution does not use the @post_load method in the schema classes to return an instance of the appropriate dataclass upon data loading (post deserialization). This is a miss as well, and crucial for tying the schema directly to the dataclass. (0/2)
Additional Aspects:
Details such as dataclass immutability (using frozen=True) and usage of nested fields in the schema are correctly handled. Example usage and data initialization are also provided, which are good practices. (2/2)

Based on the criteria and the given solution, the total score is:

{
  "score": 6
}

Response 3 (6.0)

To convert the provided JSON into dataclasses using the Marshmallow library, you can define a schema that corresponds to your JSON structure. Here's how you can do it:

First, you need to install the marshmallow library if you haven't done so already. You can do this with pip:

pip install marshmallow

Next, you can create a schema that matches your JSON:

from marshmallow import Schema, fields
from marshmallow.fields import Decimal as MarshmallowDecimal
from datetime import datetime
import decimal

class OrderSchema(Schema):
    created = fields.DateTime(format='iso')
    amount = MarshmallowDecimal(as_string=True)

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)

Now, you can use this schema to deserialize your JSON:

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

user_schema = UserSchema()
result = user_schema.load(json_data)

print(result)

This will output:

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

The load method deserializes the JSON into Python objects.

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) (0.94¢)

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

Defining two dataclasses:
The solution doesn't define any dataclasses. (0/2 marks)
Defining two schema classes:
The solution correctly defines OrderSchema and UserSchema. (2/2 marks)
Specifying date format for date_of_birth:
The solution correctly specifies the date format as '%m/%d/%Y'. (1/1 mark)
Decimal field with as_string=True:
The solution correctly uses as_string=True for the amount field. (1/1 mark)
Linking schema and dataclasses with @post_load:
The solution doesn't use @post_load to link schemas with dataclasses. (0/2 marks)
Additional considerations:
The solution imports necessary modules.
It provides an example of how to use the schema to deserialize JSON data.
The output demonstrates correct conversion of types (date, datetime, decimal).
(2/2 marks)

Comments:
The solution correctly implements the schema classes using Marshmallow and handles the required field types. However, it doesn't create dataclasses or use @post_load to convert the deserialized data into dataclass instances. The solution focuses on schema definition and deserialization, which partially addresses the problem but misses the dataclass aspect.

Mark breakdown:

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

Score:

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

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

The evaluation based on the given marking scheme:

Definition of two dataclasses for the person and the order: The provided solution has not defined any dataclasses. It has only defined schema classes.
Score: 0/2
Definition of two schema classes for the person and the order: The solution has successfully defined two schema classes named UserSchema and OrderSchema.
Score: 2/2
Specification of the date format for date_ofbirth field: The solution correctly specifies the date format by using the fields.Date(format='%m/%d/%Y') in the UserSchema.
Score: 1/1
The decimal field with as_string=True requirement: The solution specifies the amount field in the OrderSchema properly using MarshmallowDecimal(as_string=True).
Score: 1/1
Schema and dataclasses linked with a @post_load method: The solution lacks any @post_load methods to link schema validation/output to dataclasses.
Score: 0/2
Other considerations (e.g., installation instructions, correct usage of schema to load and print data): The solution provides a good explanation on how to use the schema and includes setup instructions. However, it fails to demonstrate practical integration with dataclasses and omits the step to convert loaded data to proper dataclass instances.
Score: 1/2

Based on the provided details and the marking scheme:

{
  "score": 5
}

1.00	0.84	0.80
0.84	1.00	0.82
0.80	0.82	1.00

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