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Изворен канал @pythonotes · Post #397 · 12 ное.

Использование Pydantic сегодня стало нормой, и это правильно. Но иногда на ревью вижу, что используют его не всегда корректно. Например, метод BaseModel.model_dump() по умолчанию не преобразует стандартные типы, такие как datetime, UUID или Decimal, в простой сериализуемый для JSON вид. Тогда пишут кастмоный сериализатор для этих типов чтобы функция json.dump() не падала с ошибкой. import uuid from datetime import datetime from decimal import Decimal from uuid import UUID from pydantic import BaseModel class MyModel(BaseModel): id: UUID date: datetime value: Decimal obj = MyModel( id=uuid.uuid4(), date=datetime.now(), value='1.23' ) print(obj.model_dump()) # не подходит для json.dump # { # 'id': UUID('4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1'), # 'date': datetime.datetime(2025, 12, 12, 12, 12, 12, 111111), # 'value': Decimal('1.23') # } # добавляем свой кастомный сериализатор json.dumps(obj.model_dump(), cls=MySerializer) # { # 'id': '4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1', # 'date': '2025-12-12T12:12:12.111111', # 'value': '1.23' # } В данном случае класс MySerializer обрабатывает datetime, UUID и Decimal. Например так: class MySerializer(json.JSONEncoder): def default(self, o): if isinstance(o, Decimal): return str(o) elif isinstance(o, datetime): return o.isoformat() elif isinstance(o, UUID): return str(o) return super().default(o) Специально для тех, кто всё еще так делает - в этом нет необходимости! Pydantic может это сделать сам, просто нужно добавить параметр mode="json". json.dumps(obj.model_dump(mode="json")) # { # 'id': '4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1', # 'date': '2012-12-12T12:12:12.111111', # 'value': '1.23' # } #pydantic#libs

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@venturevillagewall · Post #4047 · 01.02.2025 г., 16:00

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Funding Round Highlights: January 2025 🔍 Key funding rounds from January 2025: - Omnitron Sensors: $13M for efficient MEMS manufacturing. Learn More - Unwrap.ai: $12M for AI-powered customer feedback analytics. Learn More - Le Fourgon: $8.55M for returnable product transportation. - TeamSec: $7.60M for cloud-based securitization solutions. Learn More - Crowded: $7.50M raised. - Cactos: $7.27M for innovative electricity storage solutions. Learn More - Selta Square: $6.41M for pharmacovigilance AI solutions. Learn More - Medsender: $5M for HIPAA-compliant document automation. Learn More - Univale Transportes: $4.78M raised. - SKUsafe: $4.30M for compliance and nutritional analysis tools. Learn More #AI#Fintech#Regtech#Manufacturing#Healthcare#Energy#SupplyChain#NutritionalAnalysis#Compliance#StartupFunding

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@venturevillagewall · Post #3419 · 18.12.2024 г., 12:11

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Tyme Raises $250M Round #Tyme has successfully raised a substantial $250 million in funding, closing the round on December 17, 2024. For more details, visit Tyme's website. #Funding#Tyme#Investment#Capital#Finance#Business#Technology#Startups#FundingRound#VentureCapital #2024 #Growth#Innovation#CapitalRaising#TechNews#Market#Entrepreneurship#StartUpFunding#FinancialServices

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@venturevillagewall · Post #3912 · 18.01.2025 г., 10:00

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$51M Raised Across Multiple Startups Ten innovative startups secured funding in January 2025, totaling $51 million. Highlights include: - Merit Systems: $10M for an open-source economy. - Botika: $8M, leading in AI-generated fashion models. - Underdogs: $6.13M, focused on training entrepreneurs. - TrustUp: $5.18M in health tech innovation. - Elcogen: $5M in efficient fuel cell manufacturing. - Convergence Medical Robotics: $5M for medical robotics solutions. - Constelli: $3M in signal processing for defense. - Immunocure Discovery Solutions: $3M in AI drug discovery services. - Naco: $2.58M for nano coatings in fuel cells. - TalentMapper: $2.44M for talent management solutions. Read more: Merit Systems | Botika | Underdogs | TrustUp | Elcogen | Convergence Medical Robotics | Constelli | Immunocure Discovery Solutions | Naco | TalentMapper #StartupFunding#OpenSource#AI#FashionTech#Education#HealthTech#FuelCells#Innovation#SignalProcessing#DrugDiscovery#NanoTech#HydrogenEnergy#MedTech#VC

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