306 lines
9.5 KiB
Markdown
306 lines
9.5 KiB
Markdown
# Models
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## Defining models
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By defining an ormar Model you get corresponding **Pydantic model** as well as **Sqlalchemy table** for free.
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They are being managed in the background and you do not have to create them on your own.
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### Model Class
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To build an ormar model you simply need to inherit a `ormar.Model` class.
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```Python hl_lines="10"
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--8<-- "../docs_src/models/docs001.py"
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```
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### Defining Fields
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Next assign one or more of the [Fields][fields] as a class level variables.
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Each table **has to** have a primary key column, which you specify by setting `primary_key=True` on selected field.
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Only one primary key column is allowed.
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```Python hl_lines="15 16 17"
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--8<-- "../docs_src/models/docs001.py"
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```
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!!! warning
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Not assigning `primary_key` column or assigning more than one column per `Model` will raise `ModelDefinitionError`
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exception.
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By default if you assign primary key to `Integer` field, the `autoincrement` option is set to true.
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You can disable by passing `autoincremant=False`.
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```Python
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id = ormar.Integer(primary_key=True, autoincrement=False)
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```
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### Fields names vs Column names
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By default names of the fields will be used for both the underlying `pydantic` model and `sqlalchemy` table.
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If for whatever reason you prefer to change the name in the database but keep the name in the model you can do this
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with specifying `name` parameter during Field declaration
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Here you have a sample model with changed names
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```Python hl_lines="16-19"
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--8<-- "../docs_src/models/docs008.py"
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```
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Note that you can also change the ForeignKey column name
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```Python hl_lines="9"
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--8<-- "../docs_src/models/docs009.py"
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```
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But for now you cannot change the ManyToMany column names as they go through other Model anyway.
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```Python hl_lines="18"
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--8<-- "../docs_src/models/docs010.py"
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```
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### Database initialization/ migrations
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Note that all examples assume that you already have a database.
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If that is not the case and you need to create your tables, that's super easy as `ormar` is using sqlalchemy for underlying table construction.
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All you have to do is call `create_all()` like in the example below.
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```python
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import sqlalchemy
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# get your database url in sqlalchemy format - same as used with databases instance used in Model definition
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engine = sqlalchemy.create_engine("sqlite:///test.db")
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# note that this has to be the same metadata that is used in ormar Models definition
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metadata.create_all(engine)
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```
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You can also create single tables, sqlalchemy tables are exposed in `ormar.Meta` class.
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```python
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import sqlalchemy
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# get your database url in sqlalchemy format - same as used with databases instance used in Model definition
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engine = sqlalchemy.create_engine("sqlite:///test.db")
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# Artist is an ormar model from previous examples
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Artist.Meta.table.create(engine)
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```
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!!!warning
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You need to create the tables only once, so use a python console for that or remove the script from your production code after first use.
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Likewise as with tables, since we base tables on sqlalchemy for migrations please use [alembic][alembic].
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Use command line to reproduce this minimalistic example.
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```python
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alembic init alembic
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alembic revision --autogenerate -m "made some changes"
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alembic upgrade head
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```
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!!!info
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You can read more about table creation, altering and migrations in [sqlalchemy table creation][sqlalchemy table creation] documentation.
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### Dependencies
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Since ormar depends on [`databases`][databases] and [`sqlalchemy-core`][sqlalchemy-core] for database connection
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and table creation you need to assign each `Model` with two special parameters.
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#### Databases
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One is `Database` instance created with your database url in [sqlalchemy connection string][sqlalchemy connection string] format.
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Created instance needs to be passed to every `Model` with `Meta` class `database` parameter.
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```Python hl_lines="1 6 12"
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--8<-- "../docs_src/models/docs001.py"
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```
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!!! tip
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You need to create the `Database` instance **only once** and use it for all models.
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You can create several ones if you want to use multiple databases.
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#### Sqlalchemy
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Second dependency is sqlalchemy `MetaData` instance.
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Created instance needs to be passed to every `Model` with `Meta` class `metadata` parameter.
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```Python hl_lines="2 7 13"
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--8<-- "../docs_src/models/docs001.py"
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```
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!!! tip
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You need to create the `MetaData` instance **only once** and use it for all models.
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You can create several ones if you want to use multiple databases.
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### Table Names
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By default table name is created from Model class name as lowercase name plus 's'.
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You can overwrite this parameter by providing `Meta` class `tablename` argument.
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```Python hl_lines="12 13 14"
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--8<-- "../docs_src/models/docs002.py"
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```
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### Constraints
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On a model level you can also set model-wise constraints on sql columns.
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Right now only `UniqueColumns` constraint is present.
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!!!tip
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To read more about columns constraints like `primary_key`, `unique`, `ForeignKey` etc. visit [fields][fields].
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You can set this parameter by providing `Meta` class `constraints` argument.
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```Python hl_lines="14-17"
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--8<-- "../docs_src/models/docs006.py"
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```
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## Initialization
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There are two ways to create and persist the `Model` instance in the database.
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!!!tip
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Use `ipython` to try this from the console, since it supports `await`.
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If you plan to modify the instance in the later execution of your program you can initiate your `Model` as a normal class and later await a `save()` call.
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```Python hl_lines="20 21"
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--8<-- "../docs_src/models/docs007.py"
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```
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If you want to initiate your `Model` and at the same time save in in the database use a QuerySet's method `create()`.
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For creating multiple objects at once a `bulk_create()` QuerySet's method is available.
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Each model has a `QuerySet` initialised as `objects` parameter
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```Python hl_lines="23"
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--8<-- "../docs_src/models/docs007.py"
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```
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!!!info
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To read more about `QuerySets` (including bulk operations) and available methods visit [queries][queries]
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## `Model` methods
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### load
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By default when you query a table without prefetching related models, the ormar will still construct
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your related models, but populate them only with the pk value. You can load the related model by calling `load()` method.
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`load()` can also be used to refresh the model from the database (if it was changed by some other process).
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```python
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track = await Track.objects.get(name='The Bird')
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track.album.pk # will return malibu album pk (1)
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track.album.name # will return None
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# you need to actually load the data first
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await track.album.load()
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track.album.name # will return 'Malibu'
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```
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### save
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You can create new models by using `QuerySet.create()` method or by initializing your model as a normal pydantic model
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and later calling `save()` method.
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`save()` can also be used to persist changes that you made to the model.
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```python
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track = Track(name='The Bird')
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await track.save() # will persist the model in database
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```
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### delete
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You can delete models by using `QuerySet.delete()` method or by using your model and calling `delete()` method.
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```python
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track = await Track.objects.get(name='The Bird')
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await track.delete() # will delete the model from database
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```
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!!!tip
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Note that that `track` object stays the same, only record in the database is removed.
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### update
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You can delete models by using `QuerySet.update()` method or by using your model and calling `update()` method.
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```python
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track = await Track.objects.get(name='The Bird')
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await track.update(name='The Bird Strikes Again')
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```
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## Internals
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Apart from special parameters defined in the `Model` during definition (tablename, metadata etc.) the `Model` provides you with useful internals.
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### Pydantic Model
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All `Model` classes inherit from `pydantic.BaseModel` so you can access all normal attributes of pydantic models.
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For example to list pydantic model fields you can:
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```Python hl_lines="20"
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--8<-- "../docs_src/models/docs003.py"
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```
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!!!tip
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Note how the primary key `id` field is optional as `Integer` primary key by default has `autoincrement` set to `True`.
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!!!info
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For more options visit official [pydantic][pydantic] documentation.
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### Sqlalchemy Table
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To access auto created sqlalchemy table you can use `Model.Meta.table` parameter
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For example to list table columns you can:
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```Python hl_lines="20"
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--8<-- "../docs_src/models/docs004.py"
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```
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!!!tip
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You can access table primary key name by `Course.Meta.pkname`
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!!!info
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For more options visit official [sqlalchemy-metadata][sqlalchemy-metadata] documentation.
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### Fields Definition
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To access ormar `Fields` you can use `Model.Meta.model_fields` parameter
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For example to list table model fields you can:
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```Python hl_lines="19"
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--8<-- "../docs_src/models/docs005.py"
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```
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!!!info
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Note that fields stored on a model are `classes` not `instances`.
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So if you print just model fields you will get:
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`{'id': <class 'ormar.fields.model_fields.Integer'>, `
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`'name': <class 'ormar.fields.model_fields.String'>, `
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`'completed': <class 'ormar.fields.model_fields.Boolean'>}`
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[fields]: ./fields.md
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[relations]: ./relations.md
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[queries]: ./queries.md
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[pydantic]: https://pydantic-docs.helpmanual.io/
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[sqlalchemy-core]: https://docs.sqlalchemy.org/en/latest/core/
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[sqlalchemy-metadata]: https://docs.sqlalchemy.org/en/13/core/metadata.html
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[databases]: https://github.com/encode/databases
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[sqlalchemy connection string]: https://docs.sqlalchemy.org/en/13/core/engines.html#database-urls
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[sqlalchemy table creation]: https://docs.sqlalchemy.org/en/13/core/metadata.html#creating-and-dropping-database-tables
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[alembic]: https://alembic.sqlalchemy.org/en/latest/tutorial.html |