gostmodels 0.2.0

Python pydantic models wrapper

ElasticModel

ElasticModel is a wrapper around pydantic.BaseModel(v2) designed to simplify working with partial (projected) data from databases or APIs.

The key advantage is support for nested models ("model within a model") without requiring all fields to be loaded. Despite this, model methods remain accessible at every level and operate on the data that has been loaded.

⚠️This behavior wouldn't be possible with Pydantic, which requires all fields to be present.

Core features of ElasticModel:

• ✅ Fully inherits the behavior of pydantic.BaseModel: all methods and functionality. Just replace BaseModel with ElasticModel.
• ✅ Allows creating models from incomplete data and provides full access — read fields, call methods, even on nested models.
• ✅ Avoids the need for declaring a bunch of Optional fields.
• ✅ Supports dynamic (undeclared) fields — stored in self.elastic_extra, a dictionary for unprocessed data.
• ✅ Ensures strict access: only loaded fields can be accessed. Trying to access an unloaded field will raise NotLoadedFieldError.
• ✅ Supports recursive creation of nested models.
• ✅ Provides shallow or deep checks for required fields at any time (on demand).

ElasticModel combines the best of BaseModel.model_validate (structured/nested models) and BaseModel.model_construct (creation without validation), adding new flexibility for working with partial data — without overloading your codebase with Optional fields.

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🌐Переклад тут🔱 ElasticModel — це `pydantic.BaseModel`(v2), який надає ... та спрощує...

... спрощує роботу з частковими (проекційними) даними з баз даних або API.

Основна перевага — підтримка вкладених моделей ("модель у моделі") без необхідності завантаження всіх полів. Незважаючи на це, методи моделі залишаються доступними на всіх рівнях і працюють із тими даними, які були завантажені.

⚠️У Pydantic така поведінка була б неможливою, оскільки він вимагає наявності всіх полів.

Основні можливості ElasticModel:

• ✅ Повністю наслідує поведінку pydantic.BaseModel: усі методи та функціональність. Просто заміни BaseModel на ElasticModel.
• ✅ Дозволяє створювати моделі з неповними даними й отримувати повний доступ — читати поля, викликати методи, навіть на вкладених моделях.
• ✅ Не потребує великої кількості полів типу Optional.
• ✅ Підтримує динамічні (неописані) поля — вони зберігаються в self.elastic_extra, словнику для необроблених даних.
• ✅ Гарантує контроль доступу: можна звертатись лише до завантажених полів. Спроба доступу до незавантаженого поля викличе NotLoadedFieldError.
• ✅ Підтримує рекурсивне створення вкладених моделей.
• ✅ Дозволяє виконувати поверхневу або глибоку перевірку обов’язкових полів у будь-який момент (на вимогу користувача).

ElasticModel поєднує найкраще з BaseModel.model_validate (структуровані/вкладені моделі) та BaseModel.model_construct (створення без валідації), додаючи нову гнучкість у роботі з частковими даними — без перевантаження кодової бази полями Optional.

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Install

pip install gostmodels

https://pypi.org/project/gostmodels/

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Quick start

from typing import Annotated
from datetime import datetime
from pydantic import Field, EmailStr

from gostmodels import ElasticModel, NotLoadedFieldError

class Created(ElasticModel):
    at: str
    by: str
    def datetime_from_at(self) -> datetime:
        return datetime.strptime(self.at, "%Y-%m-%d")

class User(ElasticModel):
    id: str = Field(alias="_id")
    first_name: Annotated[str, Field(min_length=2)]
    last_name: str
    email: EmailStr
    created: Created
    updated: Created
    
    def welcome(self) -> str:
        # Uses only fields present in the example payload below
        return f"Hi {self.first_name}! Joined at {self.created.datetime_from_at()}"

# Build from a projection (partial dict)
doc = {
    "_id": "u1",
    "first_name": "Ann",
    "email": "[e-mail]",
    "created": {
        "at": "2025-08-15"
        # "by": missing 
    },
    "updated": {
        "at": "2099-01-10"
        # "by": missing 
    },
    "external_value": 1,   # unknown key → goes to .extra
}
# --------MAIN CONSTRUCTOR-------
u = User.elastic_create(doc)        # ✅ -> ElasticModel
# -------------------------------
assert u.id == "u1"   # Alias works; unknown keys preserved without validation

# 💡 .elastic_extra is a simple dict that stores all unknown field models 💡
print(u.elastic_extra)                      # ✅ -> {'external_value': 1}

# 💡 Nested model is constructed, so methods on nested instances are available
# Model methods can operate with currently loaded data
print(u.created.datetime_from_at()) # ✅ -> "2025-08-15 00:00:00"  (type <class 'datetime.datetime')
print(u.welcome())                  # ✅ -> "Hi Ann Lee! Joined at 2025-08-15 00:00:00"

# 💡 Accessing a declared but not loaded field → NotLoadedFieldError
print(u.created.by)                 # ❌ -> ERROR NotLoadedFieldError


# .is_loaded(key) - Safe verification of field presence in the model
assert u.created.elastic_is_loaded("by") == False
u.created.by = "system"  # Mark fields as loaded by assigning to them
assert u.created.elastic_is_loaded("by") == True


# 💡 Choose validation depth when you need it
# shallow (recursive=False): do not descend into nested models
ok_shallow, bad_paths = u.elastic_is_valid(recursive=False)
print(ok_shallow, bad_paths)    # ✅ -> True, []

# deep (recursive=True): checks nested models and finds missing required field in "updated"
ok_deep, bad_paths = u.elastic_is_valid(recursive=True)
print(ok_deep, bad_paths)       # ⚠️ -> False, ['updated.by']


# Produce a fully validated pydantic.BaseModel instance (or raise ValidationError)
u.updated.by = "user"  # Before making the pydantic model, we fill in the missing field to avoid getting a ValidationError
validated = u.elastic_get_validated_model(recursive=True)   # ✅ -> pydantic.BaseModel

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Comparing .elastic_create to .model_validate and .model_construct from pydantic

from datetime import datetime
from pydantic import BaseModel, EmailStr, ValidationError
from gostmodels import ElasticModel

# Compare the methods of creating objects using different approaches:
# 1. pydantic.BaseModel.model_validate
# 2. pydantic.BaseModel.model_construct
# 3. gostmodels.ElasticModel.elastic_create

# Let's create identical BaseModel and ElasticModel model:
# - pydantic.BaseModel
# -------------------------------
class CreatedPydantic(BaseModel):
    at: str
    by: str
    def datetime_from_at(self) -> datetime:
        return datetime.strptime(self.at, "%Y-%m-%d")

class UserPydantic(BaseModel):
    email: EmailStr
    created: CreatedPydantic
# -------------------------------
# - gostmodels.ElasticModel
# -------------------------------
class CreatedElastic(ElasticModel):
    at: str
    by: str
    def datetime_from_at(self) -> datetime:
        return datetime.strptime(self.at, "%Y-%m-%d")

class UserElastic(ElasticModel):
    email: EmailStr
    created: CreatedElastic
# -------------------------------

# Equally limited data, but enough for the actions we need
partial_data = {
    "email": "[e-mail]",
    "created": {
        "at": "2025-08-15"
        # "by": missing 
        }
    }

# 1. pydantic.model_validate → raises immediately                       
user_validate = UserPydantic.model_validate(partial_data)       # ❌ -> ERROR ValidationError: 1 validation error for UserPydantic

# 2. pydantic.model_construct → does not validate, but keeps nested dicts
user_construct = UserPydantic.model_construct(**partial_data)   # ✅
assert isinstance(user_construct.created, dict)                 # ⚠️ -> raw dict; methods relying on CreatedPydantic would break
print(user_construct.created.datetime_from_at())                # ❌ -> ERROR AttributeError: 'dict' object has no attribute 'datetime_from_at

# 3. ElasticModel.elastic_create → no instant failures, and nested models are created
user_elastic = UserElastic.elastic_create(partial_data)         # ✅
assert isinstance(user_elastic.created, CreatedElastic)         # ✅
print(user_elastic.created.datetime_from_at())                  # ✅ -> 2025-08-15 00:00:00

Summary:

• model_validate: full validation + nested building, but no partials
• model_construct: partials OK, but nested dicts remain dicts
• elastic_create: partials OK + nested building + strict read access + shallow/deep validation

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Key features

• Partial construction: elastic_create(data, validate=True, apply_defaults=False)

  • Accepts dicts with missing and extra keys
  • Validates/coerces values via TypeAdapter using your type hints (including Annotated[..., Field(...)])
  • Unknown keys are captured in model.extra
  • Tracks actually loaded fields in ._loaded_fields
  • apply_defaults=True applies default/default_factory to missing fields and marks them as loaded

• Strict read access

  • Accessing an unloaded declared field raises NotLoadedFieldError
  • System attributes and dunders are not intercepted

• Shallow vs Deep validation

  • Shallow: keep existing nested instances, fast
  • Deep: fully materialize to plain structures and validate everything

• Nested models and containers

  • Nested ElasticModel fields are built via elastic_create
  • list/set/tuple items are coerced recursively (when validate=True)
  • dict[K, V] keys and values are validated (when validate=True)

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API snapshot

• ElasticModel.elastic_create(data: dict, *, validate: bool = True, apply_defaults: bool = False) -> Self
• model.elastic_extra -> dict[str, any]
• model.elastic_is_loaded(name: str) -> bool
• model.elastic_is_valid(*, recursive: bool = True) -> tuple[bool, list[str]]
• model.elastic_get_validated_model(recursive: bool = True) -> Self
• Assignment marks fields as loaded: model.field = value

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Defaults and config

ElasticModel sets these pydantic.ConfigDict defaults:

• extra='ignore' — extra keys are ignored by Pydantic but manually collected into .extra
• populate_by_name=True — supports both field names and aliases
• revalidate_instances='never' — nested model instances are not revalidated automatically (important for shallow validation)