Perplexity: Llama3 Sonar 8B 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 (8.0)

To generate dataclasses for the JSON object, you can use the marshmallow-recipe library. First, install the library using pip:

pip install marshmallow-recipe

Now, you can define your dataclasses:

import datetime
from decimal import Decimal
from dataclasses import dataclass
from marshmallow_recipe import marshmallow_recipe

@dataclass(frozen=True)
class User:
    first_name: str
    last_name: str
    date_of_birth: datetime.date
    orders: dict  # Decimals don't work directly in dicts, so we'll use a dict representation

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

@marshmallow_recipe(num_digits_after_decimal=2, level=0)
class UserSchema:
    class Config:
        ordered = True

    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()
    orders = fields.Nested('OrderSchema', many=True)

@marshmallow_recipe(num_digits_after_decimal=2, level=0)
class OrderSchema:
    class Config:
        ordered = True

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

# Use these schema classes for serialization and deserialization
user_schema = UserSchema()
order_schema = OrderSchema()

This approach ensures you handle dates, timestamps, and decimals correctly while implementing matching schema classes.

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: (8.0) (1.00¢)

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

The solution defines two dataclasses, User and Order. (2 marks)
The solution defines two schema classes, UserSchema and OrderSchema. (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 solution uses @marshmallow_recipe decorator instead of explicitly defining @post_load methods. While this is a valid approach, it doesn't directly meet the requirement of using @post_load. (1 mark)
Additional points:
The solution uses marshmallow-recipe, which is an extension of marshmallow, showing knowledge of relevant libraries.
It correctly handles the created field as a DateTime.
It uses frozen=True for dataclasses, which is a good practice for immutability.
(2 marks)

Breakdown of marks:

{
  "Dataclasses definition": 2,
  "Schema classes definition": 2,
  "Date format specification": 0,
  "Decimal field configuration": 1,
  "Schema and dataclass linking": 1,
  "Additional points": 2
}

Total score:

{
  "scores": [2, 2, 0, 1, 1, 2],
  "total_score": 8
}

Comments:
The solution provides a good approach to the problem using marshmallow-recipe, which simplifies the process. However, it misses a few key requirements from the marking scheme. The date format for date_of_birth is not specified, and the linking between schemas and dataclasses is done implicitly through the marshmallow_recipe decorator rather than explicitly with @post_load methods. Despite these shortcomings, the solution demonstrates a good understanding of working with complex data types and schema definition in Python.

openai/gpt-4-0125-preview says: (4.0) ()

The solution provided uses the marshalellow-recipe library, a fictitious variation of the intended marshmallow library, which is a popular serialization and deserialization library in Python. This mistake suggests a misunderstanding or typo and indicates that the correct library wasn't directly referenced. Let's evaluate the approach based on the marking scheme:

Two dataclasses defined (User, Order):
The solution includes two dataclasses as required. Each class is annotated with appropriate types, although the orders field in the User class is incorrectly typed as dict instead of a list of Order instances. This is a minor error impacting functionality.
Score: 1.5/2
Two schema classes defined (UserSchema, OrderSchema):
The solution does define two schema classes, but uses marshmallow_recipe which is based on incorrect library information. Additionally, the use of marshmallow_recipe instead of fields from marshmallow for defining fields is incorrect.
Score: 1/2
Specifies the date format for date_of_birth:
Although the solution uses the fields.Date() for the date_of_birth, it does not specify the format explicitly in the schema (format="%m/%d/%Y" as required by the problem statement).
Score: 0/1
Decimal field with as_string=True:
The solution correctly uses as_string=True for the amount field in OrderSchema, which aligns with the requirement.
Score: 1/1
Link dataclasses and schemas using a @post_load method:
The solution does not implement the @post_load decorator or method to link dataclasses with the schema output. This is a critical functionality for converting loaded data into dataclass instances in marshmallow.
Score: 0/2
Other considerations (handling of formats, correct imports, use of actual marshmallow functionalities):
- The imports do not correctly reference fields from marshmallow.
- The orders field should be a list of Order, not dict.
- The incorrect use of a non-existent library (marshmallow_recipe).
Score: 0.5/2

Overall, the approach shows an attempt to structure the solution according to the problem’s requirements but fails notably due to incorrect library usage and missing critical implementations such as proper field specifications and linking schemas and dataclasses.

Final Score:

{
  "score": 4/10
}

Response 2 (6.0)

Using the Marshmallow library in Python, you can define dataclasses to generate JSON data from a JSON object. Here's how you can do it:

Convert the Date:
The 'date_of_birth' field should be a date type for which Marshmallow has a built-in fields.Date class.
Convert the Created Date:
The 'created' field should be a datetime type, so we can use Marshmallow's fields.DateTime for this.
Convert the Amount:
The 'amount' field should be a decimal type to maintain proper precision. For this, we can use Marshmallow's fields.Decimal class.

Here's how these fields are used:

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

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

@dataclass
class Order:
    created: datetime
    amount: Decimal

class PersonSchema(Schema):
    first_name = fields.Str()
    last_name = fields.Str()
    date_of_birth = fields.Date()

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

person_schema = PersonSchema()
order_schema = OrderSchema()

person = Person("Jane", "Smith", datetime(1977, 3, 16))
person_data = person_schema.dump(person)
print(person_data)  # {'date_of_birth': '1977-03-16', 'first_name': 'Jane', 'last_name': 'Smith'}

resulting_order = OrderSchema().load({
    'created': '2024-01-25T15:00:04',
    'amount': Decimal('100.00')
})
print(resulting_order)  # {'amount': '100.00', 'created': '2024-01-25T15:00:04'}

Here, we have defined Person dataclass with date_of_birth as of datetime type and Order class with amount as Decimal type. Then, we created schemas for these dataclasses using Marshmallow.

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) (0.93¢)

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, PersonSchema and OrderSchema. (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 solution does not include @post_load methods to link the schemas and dataclasses. (0 marks)
Additional points:
The solution correctly imports necessary modules.
It demonstrates how to use the schemas with example data.
(1 mark)

Comments:
The solution provides a good foundation for using Marshmallow with dataclasses. However, it misses a few key points:
- The date format for date_of_birth is not specified, which may lead to incorrect parsing of the input JSON.
- The @post_load decorators are missing, which are crucial for creating dataclass instances from the parsed data.
- The solution doesn't show how to handle nested structures (the 'orders' list in the original JSON).

Here's the breakdown of marks:

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

Final score:

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

openai/gpt-4-0125-preview says: (None) ()

The provided solution defines dataclasses Person and Order and marshmallow schema classes PersonSchema and OrderSchema, which matches the requirements partially but has mistakes that need assessment:

Dataclass Definition (1/2 marks):
The dataclass Person sets the type of date_of_birth to datetime instead of date; this is incorrect based on the problem requirements.
Order is correctly defined with the right types (datetime for created and Decimal for amount).
Schema Class Definition (1/2 marks):
PersonSchema and OrderSchema define fields accordingly, but PersonSchema should have specified the format for date_of_binary as "%m/%d/%Y", which it did not.
as_string=True is correctly used in the OrderSchema for the decimal field amount.
Use of @post_load Method (0/2 marks):
There is no use of the @post_load method to connect the schema definitions back to dataclasses. This omission means that the solution lacks proper deserialization back into dataclass instances.
Date Format Specification (0/1 mark):
The format for the date_of_birth was not specified though this was a specific requirement (format="%m/%d/%Y").
Correct Type Handling and Extras (2/2 marks):
The types DateTime and Decimal are used accurately for created and amount fields respectively.
The example usage of dumping and loading data also effectively illustrates usage, despite the mistakes in definitions.
Comment and Example Usage:
The final provided example is useful for understanding the practical implementation and contains correct function calls despite the issues due to incorrect types in data classes and schemas.

Based on this evaluation, here's the final score:

{
  "socket": 4
}

Response 3 (6.0)

To generate dataclasses and a marshmallow schema from a JSON object, you can use the marshmallow-recipe library in Python. Here's the step-by-step guide:

Install marshmallow-recipe:
shell pip install marshmallow-recipe
Import the Libraries:
python import dataclasses import datetime import decimal from marshmallow_recipe import mr from typing import Annotated
Define Your Dataclasses:
```python
@dataclasses.dataclass(frozen=True)
class Person:
first_name: str
last_name: str
date_of_birth: datetime.date
orders: list[dict]

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

Create a Schema:
```python
class PersonSchema(mr.Schema):
class Meta:
ordered = True
first_name = mr.Str()
last_name = mr.Str()
date_of_birth = mr.Date()
orders = mr.Nested('OrderSchema', many=True, missing=mr.MISSING)

class OrderSchema(mr.Schema):
class Meta:
ordered = True
created = mr.DateTime(export_name='created_at')
amount = mr.Decimal(places=2)
```

Use the Schema:
```python
# Serialize a parsed JSON object to a dictionary
raw = mr.dump(Person(
first_name='John Doe',
last_name='mr. Smith',
date_of_birth=datetime.date(1977, 3, 16),
orders=[Order(created=datetime.datetime(2024, 1, 25), amount=decimal.Decimal('100.00'))]
))

# Deserialize a dictionary back to a Python object
loaded = mr.load(PersonSchema, raw)
```

This example demonstrates how to use marshmallow-recipe to streamline the serialization and deserialization process when dealing with complex dataclasses. For simplicity, we only include two dataclasses (Person and Order) and their schema definitions. You can extend these examples to accommodate more complex data structures.

For detailed documentation on the available fields and configuration options, refer to the marshmallow documentation and the usage examples provided in the library's readthedocs page.

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: (5.0) ()

Evaluation of the Provided Solution:

Dataclasses Definition:
The solution defines two dataclasses: Person and Order.
Each dataclass seems to have the right type annotations for date_of MSRbirth (datetime.date) and created (datetime.datetime) which is good for the Order class while amount is annotated with decimal.Decimal.
Score for this section: 2/2
Schema Classes Definition:
Two schema classes PersonSchema and OrderSchema are correctly defined.
They use the marshmallow-recipe library (mr.Schema), however, this is not traditionally part of the marshmallow core library but it is acceptable to evaluate it in the same context since it is based on marshmallow.
Score for this section: 2/2
Date Format Specification:
The solution does not specify the date format for the date_of_birth using the format="%m/%d/%Y" as required.
Score for this section: 0/1
Decimal Field as String:
The solution does not specify the amount field for as_string=True.
Score for this section: 0/1
Linkage of Schema and Dataclasses via @post_load:
There is no @post_load method used in the solution to connect the schema output back into the dataclasses, which is a critical omission for the complete deserialization process.
Score for this section: 0/2
Additional Elements:
There is no demonstration or provision of error handling or setups like specifying precise marshmallow fields such as Decimal(as_string=True) or Date(format='%m/%d/%Y').
Usage of a hyphenated attribute in a library (marshmallow-recipe) that is not standard without specifying this deviation or addressing the potential misunderstandings.
Score for this section: 1/2

Total Score Calculation:
[ \text{Dataclasses Definition} (2/2)
+ \text{Schema Classes Definition} (2/2)
+ \text{Date Format Specification} (0/1)
+ \text{Decimal Field as String} (0/1)
+ \text{Linkage of Schema and Dataclasses via @post_load} (0/2)
+ \text{Anything else} (1/2) = 5/10 ]

{
  "score": 5
}

anthropic/claude-3.5-sonnet says: (6.0) (0.91¢)

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

Two dataclasses defined (Person and Order): 2 marks
Two schema classes defined (PersonSchema and OrderSchema): 2 marks
Date format for date_of_birth field: 0 marks (not specified)
Decimal field with as_string=True: 0 marks (not included)
Schema and dataclasses linked with @post_load method: 0 marks (not implemented)
Other considerations:
Use of marshmallow-recipe library: 1 mark
Correct import statements: 1 mark

Here's the breakdown of marks:

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

Comments on the solution:
- The solution uses marshmallow-recipe instead of plain marshmallow, which is a different approach but still valid.
- The date format for date_of_birth is not specified as required.
- The decimal field does not include as_string=True.
- There's no @post_load method to link schemas and dataclasses.
- The solution includes serialization and deserialization examples, which is helpful but not part of the requirements.

Score:

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

The solution scores 6 out of 10 based on the marking scheme provided.

1.00	0.84	0.84
0.84	1.00	0.85
0.84	0.85	1.00

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