JSON Fields for Nested Pydantic Models?

[scuervo91]

First Check

• I added a very descriptive title to this issue.
• I used the GitHub search to find a similar issue and didn't find it.
• I searched the SQLModel documentation, with the integrated search.
• I already searched in Google "How to X in SQLModel" and didn't find any information.
• I already read and followed all the tutorial in the docs and didn't find an answer.
• I already checked if it is not related to SQLModel but to Pydantic.
• I already checked if it is not related to SQLModel but to SQLAlchemy.

Commit to Help

• I commit to help with one of those options 👆

Example Code

from tortoise.models import Model 
from tortoise.fields import UUIDField, DatetimeField,CharField, BooleanField, JSONField, ForeignKeyField, CharEnumField, IntField
from tortoise.contrib.pydantic import pydantic_model_creator

class Schedule(Model):
    id = UUIDField(pk=True)
    created_at = DatetimeField(auto_now_add=True)
    modified_at = DatetimeField(auto_now=True)
    case = JSONField()
    type = CharEnumField(SchemasEnum,description='Schedule Types')
    username = ForeignKeyField('models.Username')
    description = CharField(100)
    
schedule_pydantic = pydantic_model_creator(Schedule,name='Schedule')

Description

I have already implemented an API using FastAPI to store Pydantic Models. These models are themselves nested Pydantic models so the way they interact with a Postgres DataBase is throught JsonField. I've been using Tortoise ORM as the example shows.

Is there an equivalent model in SQLModel?

Operating System

Linux

Operating System Details

WSL 2 Ubuntu 20.04

SQLModel Version

0.0.4

Python Version

3.8

Additional Context

No response

[OXERY]

I also wondered how to store JSON objects without converting to string. SQL Alchemy supports storing these directly

[TheJedinator]

@OXERY && @scuervo91 - I was able to get something that works Using this:

regions: dict = Field(sa_column=Column(JSON), default={'all': 'true'})

That said: this is a postgresql JSONB column in my database. But it works.

For a nested Object you could use a pydantic model as the Type and do it the same way. Hope this helps as I was having a difficult time figuring out a solution as well :)

[OXERY]

I also got it working, on SQLite and Postgresql:
mygreatfield: Dict[Any, Any] = Field(index=False, sa_column=Column(JSON))
needs from sqlmodel import Field, SQLModel, Column, JSON as well as from typing import Dict, Any

[psarka]

@TheJedinator Could you help a bit more with the nested object? I tried to "use the pydantic model as the Type" but I can't get it to work :( Here is my snippet:

from sqlalchemy import Column
from sqlalchemy.dialects.postgresql import JSONB
from sqlmodel import Field
from sqlmodel import Session
from sqlmodel import SQLModel

from engine import get_sqlalchemy_engine


class J(SQLModel):
    j: int


class A(SQLModel, table=True):
    a: int = Field(primary_key=True)
    b: J = Field(sa_column=Column(JSONB))


engine = get_sqlalchemy_engine()
SQLModel.metadata.create_all(engine)

with Session(engine) as session:
    a = A(a=1, b=J(j=1))
    session.add(a)
    session.commit()
    session.refresh(a)

Throws an error

sqlalchemy.exc.StatementError: (builtins.TypeError) Object of type J is not JSON serializable
[SQL: INSERT INTO a (b, a) VALUES (%(b)s, %(a)s)]
[parameters: [{'a': 1, 'b': J(j=1)}]]

[TheJedinator]

@psarka

j = J(j=1)
db_j = J.from_orm(j)
a = A(a=1, b=db_j)

This should resolve your issue in preparing the object for the database. What I'm seeing in the error is that the Raw Object is being included in the statement rather than the instance...

If this doesn't help I can definitely put some more time in to looking at what's going on.

[psarka]

Thank you! Unfortunately I get the same error :(

I found one workaround - registering a custom_serializer for the sqlalchemy engine, like so:

def custom_serializer(d):
    return json.dumps(d, default=lambda v: v.json())

def get_sqlalchemy_engine():
    return create_engine("postgresql+psycopg2://", creator=get_conn, json_serializer=custom_serializer)

But if there is a cleaner way, I would gladly use that instead.

[TheJedinator]

Hey @psarka

I just actually tried what I told and sorry have mislead... I did get a working solution though 😄

It was actually the opposite function that you need to use, here's the example you supplied with the amendments to make it work:

with Session(engine) as session:
    j = J(j=1)
    j_dumped = J.json(j)
    a = A(a=1, b=j_dumped)
    session.add(a)
    session.commit()
    session.refresh(a)

[psarka]

Hmm, this doesn't (or at least shouldn't) typecheck :)

But I see what you did there, essentially it's the same as registring a custom serializer, but manually.

[TheJedinator]

It does type check when you create the J Object (which it should) So if you tried to supply a string it would fail J(j="foo")

This allows for the type checking of the object, the A class requires a serialized version of J in order for it to be entered in to the database.

It is essentially the same as registering a custom serializer but allows you to be explicit about using it.

[HenningScheufler]

A hacky method with type checking that work with sqlite is

from sqlalchemy import Column
from typing import List
# from sqlalchemy.dialects.postgresql import JSONB
from sqlmodel import Field
from sqlmodel import Session
from pydantic import validator
from sqlmodel import SQLModel, JSON,create_engine

# from engine import get_sqlalchemy_engine
sqlite_file_name = "test.db"
sqlite_url = f"sqlite:///{sqlite_file_name}"

engine = create_engine(sqlite_url)


class J2(SQLModel):
    test: List[int]

class J(SQLModel):
    j: int
    nested: J2


class A(SQLModel, table=True):
    a: int = Field(primary_key=True)
    b: J = Field(sa_column=Column(JSON))

    @validator('b')
    def val_b(cls, val):
        return val.dict()

SQLModel.metadata.create_all(engine)

with Session(engine) as session:
    a = A(a=1, b=J(j=1,nested=J2(test=[100,100,100])))
    session.add(a)
    session.commit()
    session.refresh(a)

[hakanoktay]

hi,
I created a "JSON Field" based on what is written here. I am using SQLite.

from sqlmodel import SQLModel,Relationship,Field,JSON
from typing import Optional,List, Dict
from sqlalchemy import Column
from pydantic import validator


#
class J2(SQLModel):
    id: int
    title:str

#
class Companies(SQLModel, table=True):
    id:Optional[int]=Field(default=None,primary_key=True)
    name:str
    adddresses: List['J2'] = Field(sa_column=Column(JSON))


    @validator('adddresses')
    def val_b(cls, val):
        print(val)
        return val.dict()

Given error.

TypeError: Type is not JSON serializable: J2

when i print it, it returns

[J2(id=1, title='address1'), J2(id=2, title='address2')]

how can i handle that? Why is this J2 added, how can I get rid of it, i can't turn it to .dict(), i cannot serialise it... can you give an idea?

[HenningScheufler]

Does this work?

    @validator('adddresses')
    def val_b(cls, value):
        print(value)
        return [v.dict() for v in value]

[hakanoktay]

Does this work?

    @validator('adddresses')
    def val_b(cls, value):
        print(value)
        return [v.dict() for v in value]

@HenningScheufler thank you for your help, it worked perfect.

[MaximilianFranz]

Hey all,

thanks for the great advice here. Creating a the object using the classes and writing them to the DB works as expected and writes the data as a dict into a JSON field.

See this example:

class ComplexHeroField(SQLModel, table=False):
    some: str
    other: float
    more: Optional[List[str]]

class Hero(SQLModel, table=True):
    id: Optional[int] = Field(default=None, primary_key=True)
    complex_field: ComplexHeroField = Field(sa_column=Column(JSON))
    name: str
    secret_name: str
    age: Optional[int] = None

    @validator('complex_field')
    def val_complex(cls, val: ComplexHeroField):
        # Used in order to store pydantic models as dicts
        return val.dict()

    class Config:
        arbitrary_types_allowed = True

However, when reading the model from the DB using a select() I would want the JSON field to be read into a ComplexHeroField class using pydantics parse_raw or parse_obj. Because they way it's currently done (with the validator) this happens:

        statement = select(Hero)
        results = session.exec(statement)
        for hero in results:
            print(hero.complex_field.some)

       # AttributeError: 'dict' object has no attribute 'some'

Any hint how that could be achieved? Maybe via the custom-serialiser mentioned by @psarka ?

Thanks already!

[MaximilianFranz]

Something like this works, but obviously doesn't scale if we have mulitple nested models, instead of just the ComplexHeroField:

def custom_serializer(d):
    return json.dumps(d, default=lambda v: v.json())

def custom_deserialiser(d):
    return ComplexHeroField.parse_raw(d)

engine = create_engine(url_string, echo=True, json_serializer=custom_serializer, json_deserializer=custom_deserialiser)

complex_value = ComplexHeroField(some="value", other=5, more=["dd", "sdf"])
hero_1 = Hero(name="Deadpond", secret_name="Dive Wilson", complex_field=complex_value)
session.add(hero_1)
session.commit()

statement = select(Hero)
results = session.exec(statement)
for hero in results:
    print(hero.complex_field.some)
    # value 

Instead, we would need more context in the deserialiser (i.e. access to the type-hint of the field we're trying to deserialise so that we can use UseType.parse_raw().

Any hint where and how I could achieve that kind of access to the deserialisation process?

Thanks :)

[MaximilianFranz]

Hey all,

after looking at this again, I've been able to resolve it as follows.

For our sqlalchemy models we created this PydanticJSONType factory:

def pydantic_column_type(pydantic_type):
    class PydanticJSONType(TypeDecorator, Generic[T]):
        impl = JSON()

        def __init__(
            self, json_encoder=json,
        ):
            self.json_encoder = json_encoder
            super(PydanticJSONType, self).__init__()

        def bind_processor(self, dialect):
            impl_processor = self.impl.bind_processor(dialect)
            dumps = self.json_encoder.dumps
            if impl_processor:

                def process(value: T):
                    if value is not None:
                        if isinstance(pydantic_type, ModelMetaclass):
                            # This allows to assign non-InDB models and if they're
                            # compatible, they're directly parsed into the InDB
                            # representation, thus hiding the implementation in the
                            # background. However, the InDB model will still be returned
                            value_to_dump = pydantic_type.from_orm(value)
                        else:
                            value_to_dump = value
                        value = recursive_custom_encoder(value_to_dump)
                    return impl_processor(value)

            else:

                def process(value):
                    if isinstance(pydantic_type, ModelMetaclass):
                        # This allows to assign non-InDB models and if they're
                        # compatible, they're directly parsed into the InDB
                        # representation, thus hiding the implementation in the
                        # background. However, the InDB model will still be returned
                        value_to_dump = pydantic_type.from_orm(value)
                    else:
                        value_to_dump = value
                    value = dumps(recursive_custom_encoder(value_to_dump))
                    return value

            return process

        def result_processor(self, dialect, coltype) -> T:
            impl_processor = self.impl.result_processor(dialect, coltype)
            if impl_processor:

                def process(value):
                    value = impl_processor(value)
                    if value is None:
                        return None

                    data = value
                    # Explicitly use the generic directly, not type(T)
                    full_obj = parse_obj_as(pydantic_type, data)
                    return full_obj

            else:

                def process(value):
                    if value is None:
                        return None

                    # Explicitly use the generic directly, not type(T)
                    full_obj = parse_obj_as(pydantic_type, value)
                    return full_obj

            return process

        def compare_values(self, x, y):
            return x == y

    return PydanticJSONType

where recursive_custom_encoder() is pretty much the fastAPI jsonable_encoder

Using this in SQLModel as follows:

class ConnectionResistances(SQLConnectionModel, table=False):
    very_short: ResistancesInLoadDuration = ResistancesInLoadDuration()
    short: ResistancesInLoadDuration = ResistancesInLoadDuration()
    middle: ResistancesInLoadDuration = ResistancesInLoadDuration()
    long: ResistancesInLoadDuration = ResistancesInLoadDuration()
    constant: ResistancesInLoadDuration = ResistancesInLoadDuration()
    earth_quake: ResistancesInLoadDuration = ResistancesInLoadDuration()

class Connection(SQLConnectionModel, table=True):

    id: Optional[uuid.UUID] = Field(default=None, sa_column=Column(PGUUID(as_uuid=True), default=uuid.uuid4, primary_key=True))
    name: str
    comment: str
    path_to_pdf: Optional[str] = None
    resistance_values: ConnectionResistances = Field(..., sa_column=Column(pydantic_column_type(ConnectionResistances)))

Works perfectly!
That means:

• In the DB data is stored as JSON
• whenever the model is read from the DB, data is read into the pydantic class (including validation)
• whenever the the model is written into DB, the data is transformed into JSON

This could be integrated into an sqlmodel api based on the type hint alone (i.e. creating the sa_column based on the pydantic type automatically). Potentially in get_sqlachemy_type.

What do you think, @tiangolo?

[tchaton]

@tiangolo Any updates ?

[tchaton]

Hey @MaximilianFranz Would you mind sharing your entire solution, I am quite interested in trying it out, but it is missing some code pieces.

[MaximilianFranz]

Hey @MaximilianFranz Would you mind sharing your entire solution, I am quite interested in trying it out, but it is missing some code pieces.

What exactly are you missing? Happy to provide more context!

[tchaton]

The recursive_custom_encoder is missing. Ideally, a fully working example I can simply copy/paste and adapt to my use case ;)

[MaximilianFranz]

You can use jsonable_encode like such, instead of the recursive_custom_encoder

from fastapi.encoders import jsonable_encoder

also I would start with a simpler model like:

class NestedModel(SQLModel):
    some_value: str

class OuterModel(SQLModel, table=True):
    guid: str = Field(
        default=None,
        sa_column=Column(PGUUID(as_uuid=True), default=uuid.uuid4, primary_key=True),
    )
    nested: NestedModel = Field(..., sa_column=Column(pydantic_column_type(NestedModel)))

That should work!

[tchaton]

Thanks, @MaximilianFranz Let me try. My code is here: https://github.com/Lightning-AI/lightning-hpo/blob/master/lightning_hpo/commands/sweep.py#L36. Trying to store the Sweep distributions.
Do you think it would work with the recursion?
Missing parse_obj_as and ModelMetaclass.

[tchaton]

Hey @MaximilianFranz

I have made a draft PR there: https://github.com/Lightning-AI/lightning-hpo/pull/19/files. I tried but it is raising an error. Would you mind having a look?

Best,
T.C

[MaximilianFranz]

Thanks, @MaximilianFranz Let me try. My code is here: https://github.com/Lightning-AI/lightning-hpo/blob/master/lightning_hpo/commands/sweep.py#L36. Trying to store the Sweep distributions. Do you think it would work with the recursion? Missing parse_obj_as and ModelMetaclass.

Both parse_obj_as and ModelMetaClass can be imported from pydantic:

from pydantic import parse_obj_as
from pydantic.main import ModelMetaclass

As for the error, would you mind pointing me to the action that fails or post a traceback somewhere?

[MaximilianFranz]

It makes sense that it doesn't work yet. You'll have to use the ModelMetaclass as is done in my snippet above for the isinstance check. Also the import for parse_obj_as is missing, so it can't work as it is :)

[tchaton]

Hey @MaximilianFranz, I updated the code with your inputs, but it is still failing. I pushed the updated code.

  File "/Users/thomas/Documents/GitHub/LAI-lightning-hpo-App/lightning_hpo/components/servers/db/server.py", line 42, in insert_sweep
    session.commit()
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 1451, in commit
    self._transaction.commit(_to_root=self.future)
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 829, in commit
    self._prepare_impl()
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 808, in _prepare_impl
    self.session.flush()
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 3383, in flush
    self._flush(objects)
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 3523, in _flush
    transaction.rollback(_capture_exception=True)
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__
    compat.raise_(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/util/compat.py", line 208, in raise_
    raise exception
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 3483, in _flush
    flush_context.execute()
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/unitofwork.py", line 456, in execute
    rec.execute(self)
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/unitofwork.py", line 630, in execute
    util.preloaded.orm_persistence.save_obj(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py", line 245, in save_obj
    _emit_insert_statements(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py", line 1238, in _emit_insert_statements
    result = connection._execute_20(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1631, in _execute_20
    return meth(self, args_10style, kwargs_10style, execution_options)
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/sql/elements.py", line 332, in _execute_on_connection
    return connection._execute_clauseelement(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1498, in _execute_clauseelement
    ret = self._execute_context(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1862, in _execute_context
    self._handle_dbapi_exception(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 2043, in _handle_dbapi_exception
    util.raise_(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/util/compat.py", line 208, in raise_
    raise exception
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1819, in _execute_context
    self.dialect.do_execute(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 732, in do_execute
    cursor.execute(statement, parameters)
sqlalchemy.exc.InterfaceError: (sqlite3.InterfaceError) Error binding parameter 5 - probably unsupported type.
[SQL: INSERT INTO sweepconfig (distributions, sweep_id, script_path, n_trials, simultaneous_trials, requirements, script_args, framework, cloud_compute, num_nodes, logger, direction) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)]
[parameters: ('{"name": "model.lr", "distribution": "uniform", "params": {"params": {"low": "0.001", "high": "0.1"}}}', 'thomas-5e0dd935', 'train.py', 1, 1, [], [], 'pytorch_lightning', 'cpu', 1, 'wandb', 'maximize')]
(Background on this error at: https://sqlalche.me/e/14/rvf5)

[MaximilianFranz]

Hey @MaximilianFranz, I updated the code with your inputs, but it is still failing. I pushed the updated code.

  File "/Users/thomas/Documents/GitHub/LAI-lightning-hpo-App/lightning_hpo/components/servers/db/server.py", line 42, in insert_sweep
    session.commit()
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 1451, in commit
    self._transaction.commit(_to_root=self.future)
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 829, in commit
    self._prepare_impl()
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 808, in _prepare_impl
    self.session.flush()
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 3383, in flush
    self._flush(objects)
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 3523, in _flush
    transaction.rollback(_capture_exception=True)
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py", line 70, in __exit__
    compat.raise_(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/util/compat.py", line 208, in raise_
    raise exception
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/session.py", line 3483, in _flush
    flush_context.execute()
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/unitofwork.py", line 456, in execute
    rec.execute(self)
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/unitofwork.py", line 630, in execute
    util.preloaded.orm_persistence.save_obj(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py", line 245, in save_obj
    _emit_insert_statements(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/orm/persistence.py", line 1238, in _emit_insert_statements
    result = connection._execute_20(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1631, in _execute_20
    return meth(self, args_10style, kwargs_10style, execution_options)
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/sql/elements.py", line 332, in _execute_on_connection
    return connection._execute_clauseelement(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1498, in _execute_clauseelement
    ret = self._execute_context(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1862, in _execute_context
    self._handle_dbapi_exception(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 2043, in _handle_dbapi_exception
    util.raise_(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/util/compat.py", line 208, in raise_
    raise exception
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/base.py", line 1819, in _execute_context
    self.dialect.do_execute(
  File "/Users/thomas/Documents/GitHub/lightning/.venv/lib/python3.8/site-packages/sqlalchemy/engine/default.py", line 732, in do_execute
    cursor.execute(statement, parameters)
sqlalchemy.exc.InterfaceError: (sqlite3.InterfaceError) Error binding parameter 5 - probably unsupported type.
[SQL: INSERT INTO sweepconfig (distributions, sweep_id, script_path, n_trials, simultaneous_trials, requirements, script_args, framework, cloud_compute, num_nodes, logger, direction) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)]
[parameters: ('{"name": "model.lr", "distribution": "uniform", "params": {"params": {"low": "0.001", "high": "0.1"}}}', 'thomas-5e0dd935', 'train.py', 1, 1, [], [], 'pytorch_lightning', 'cpu', 1, 'wandb', 'maximize')]
(Background on this error at: https://sqlalche.me/e/14/rvf5)

To finish this, the problem ended up being a attribute of type List[str] on an SQLModel, which is not natively supported. Using the above pydantic_column_type with List[str] works however and will encode the list as json-string in order to store it to the database.

[Zaffer]

Hoping this gets merged in the near future.

[fahadshery]

I was able to setup JSONB column for postgresql database instead of JSON that comes with sqlmodel by:

from sqlalchemy.dialects.postgresql import JSONB
from sqlmodel import Field, SQLModel, Column

class test(SQLModel, table=True):
    result: dict = Field(sa_column=Column(JSONB), default={"message":"hello world"})

[ghost]

Would be great to have this in-built! CC: @tiangolo , @MaximilianFranz

[Tridagger]

@tiangolo , @MaximilianFranz

[AAraKKe]

I arrived here looking at something that I imagined might not work yet but that seems to be a feature one would expect from a library like this. The use case is simple (the solution I am not sure haha).

If you have a model with a field that is typed as a pedantic model, it seems to me that as long as you specify the column being a JSON type serialization and deserialiation should happen automatically from the database.

class MyOtherModel(BaseModel):
    name: str
    id: int

class MyModel(SQLModel, table=True):
    id = int](id: int | None = Field(default=None, primary_key=True))
    my_field: MyOtherModel = Field(sa_column=Collumn(type_=JSON)


# ...setup session...
model = session.exec(select(MyModel).where(MyModel.id == 1)).first()
print(model.my_field.name) # Should work
model.my_field = MyOtherModel(name="New name", id=123)
session.flush() # Should work

Is this something that is being considered?

[AAraKKe]

In case this helps anyone, here is how I managed to achieve it, although depending on how many of these fields you have it might impact performance a bit.

I have a model named Meetup that has a field MeetupLocation that I wanted to serialize in the database as JSON.

class Meetup(SQLModel, table=True):
    __tablename__: str = "meetups"  # type: ignore

    # ... some fields definitions ...
    location: MeetupLocation | None = Field(
        default=None,
        sa_column=Column(type_=MeetupLocation.as_mutable(JSON(none_as_null=True)), nullable=True),
    )

I will talk about how MeetupLocation has been defined in a bit.

To serialize and deserialize, I leveraged the engine using the json_serializer and json_deserializer arguments. It looks something like this

    def serialize_pydantic_model(model: BaseModel) -> str:
        return model.model_dump_json()

    def deserialize_pydantic_model(data: str) -> BaseModel | None:
        # Try deserializing with each model until one works.
        # This is a pretty ugly solution but the deserialization seems to only be possible and reliable at an engine level
        # and we need to know the model to deserialize it properly
        # We would need to keep adding more of these if we add more models with JSON fields.
        with suppress(ValidationError):
            return MeetupLocation.model_validate_json(data)
        return None

    engine = create_engine(
        db_config.full_url,
        echo=db_config.engine_echo,
        json_serializer=serialize_pydantic_model,
        json_deserializer=deserialize_pydantic_model,
    )

The key part here is that SQLModel seems to be breaking mutable tracking by SQLAlchemy, so when you later do something like

meetup.location.name = "Other name"
session.add(meetup)
session.commit()

It will not be persisted in the db because, as far as the Session cares, no modification has been made in the model.

To fix this, I have created a MutableModel that takes care of informing the Session through Mutable that the object has been updated.

from pydantic import BaseModel
from sqlalchemy.ext.mutable import Mutable


class MutableModel(BaseModel, Mutable):
    def __setattr__(self, name: str, value: Any) -> None:
        """Allows SQLAlchmey Session to track mutable behavior when updating any field"""
        self.changed()
        return super().__setattr__(name, value)

    @classmethod
    def coerce(cls, key: str, value: Any) -> Self | None:
        """Convert JSON to MeetupLocation object allowing for mutable behavior"""
        if isinstance(value, cls) or value is None:
            return value

        if isinstance(value, str):
            return cls.model_validate_json(value)

        if isinstance(value, dict):
            return cls(**value)

        super().coerce(key, value)

Then, MeetupLocation is created like this

class MeetupLocation(MutableModel):
    name: str | None = None
    location: tuple[float, float] | None = None

And this works as far as I have been able to validate it. It can read json and transform them into the we want and it can update the object in the database if anything changes in the model later on.

I hope this helps.

[copdips]

    def serialize_pydantic_model(model: BaseModel) -> str:
        return model.model_dump_json()

a little question, why not use model.model_dump() ?
and maybe a fix at Pydantic level for the deserializer would be better IMHO.

Otherwise, when I insert data into DB, the nested field is already a dict type.
In fact, during the data preparation, I have an extra step with model_validate() like:

new_row = {"name": "aa", "location": {"address": "bb", "city": "cc"}}
new_row_db = Meetup.model_validate(new_row )
# Here, new_row_db.location is a dict type.
session.add(new_row_db)

The serializer is a must have otherwise I cannot add the new row into DB, but the deserializer is optional for me, as I'm fine with dict instead of MeetupLocation.

[tcztzy]

In my very personl senario, my json data are in format like

{
  "object": "array",
  "data": [
    1,
    2
  ]  
}

Every json object must have two keys object and data, therefore I can determinate the exact MutableModel to deserilize the json string. Maybe we can store this information somewhere for nested Pydantic Models in SQLModel the help with deserilization?

In case this helps anyone, here is how I managed to achieve it, although depending on how many of these fields you have it might impact performance a bit.

I have a model named Meetup that has a field MeetupLocation that I wanted to serialize in the database as JSON.

class Meetup(SQLModel, table=True):
    __tablename__: str = "meetups"  # type: ignore

    # ... some fields definitions ...
    location: MeetupLocation | None = Field(
        default=None,
        sa_column=Column(type_=MeetupLocation.as_mutable(JSON(none_as_null=True)), nullable=True),
    )

I will talk about how MeetupLocation has been defined in a bit.

To serialize and deserialize, I leveraged the engine using the json_serializer and json_deserializer arguments. It looks something like this

    def serialize_pydantic_model(model: BaseModel) -> str:
        return model.model_dump_json()

    def deserialize_pydantic_model(data: str) -> BaseModel | None:
        # Try deserializing with each model until one works.
        # This is a pretty ugly solution but the deserialization seems to only be possible and reliable at an engine level
        # and we need to know the model to deserialize it properly
        # We would need to keep adding more of these if we add more models with JSON fields.
        with suppress(ValidationError):
            return MeetupLocation.model_validate_json(data)
        return None

    engine = create_engine(
        db_config.full_url,
        echo=db_config.engine_echo,
        json_serializer=serialize_pydantic_model,
        json_deserializer=deserialize_pydantic_model,
    )

The key part here is that SQLModel seems to be breaking mutable tracking by SQLAlchemy, so when you later do something like

meetup.location.name = "Other name"
session.add(meetup)
session.commit()

It will not be persisted in the db because, as far as the Session cares, no modification has been made in the model.

To fix this, I have created a MutableModel that takes care of informing the Session through Mutable that the object has been updated.

from pydantic import BaseModel
from sqlalchemy.ext.mutable import Mutable


class MutableModel(BaseModel, Mutable):
    def __setattr__(self, name: str, value: Any) -> None:
        """Allows SQLAlchmey Session to track mutable behavior when updating any field"""
        self.changed()
        return super().__setattr__(name, value)

    @classmethod
    def coerce(cls, key: str, value: Any) -> Self | None:
        """Convert JSON to MeetupLocation object allowing for mutable behavior"""
        if isinstance(value, cls) or value is None:
            return value

        if isinstance(value, str):
            return cls.model_validate_json(value)

        if isinstance(value, dict):
            return cls(**value)

        super().coerce(key, value)

Then, MeetupLocation is created like this

class MeetupLocation(MutableModel):
    name: str | None = None
    location: tuple[float, float] | None = None

And this works as far as I have been able to validate it. It can read json and transform them into the we want and it can update the object in the database if anything changes in the model later on.

I hope this helps.

[felipemonroy]

I was able to solve this issue using the json-fix library. You only need to add the following method to your nested models.

    def __json__(self):
        return self.model_dump()

I hope this can be done without an external library.

[alexdashly]

This is my approach using a conventional SQLAlchemy approach. This is actually how others have done this with SA and Pydantic, for instance in this discussion: sqlalchemy/sqlalchemy#11050

from typing import Any, Self

from pydantic import BaseModel as _BaseModel
from sqlalchemy import JSON, types, Column
from sqlalchemy.ext.mutable import Mutable
from sqlmodel import SQLModel, Field


class JsonPydanticField(types.TypeDecorator):
    impl = JSON

    def __init__(self, pydantic_model):
        super().__init__()
        self.pydantic_model = pydantic_model

    def load_dialect_impl(self, dialect):
        return dialect.type_descriptor(JSON())

    def process_bind_param(self, value: _BaseModel, _):
        return value.model_dump() if value is not None else None

    def process_result_value(self, value, _):
        return self.pydantic_model.model_validate(value) if value is not None else None


class MutableSABaseModel(_BaseModel, Mutable):

    def __setattr__(self, name: str, value: Any) -> None:
        """Allows SQLAlchmey Session to track mutable behavior"""
        self.changed()
        return super().__setattr__(name, value)

    @classmethod
    def coerce(cls, key: str, value: Any) -> Self | None:
        """Convert JSON to pydantic model object allowing for mutable behavior"""
        if isinstance(value, cls) or value is None:
            return value

        if isinstance(value, str):
            return cls.model_validate_json(value)

        if isinstance(value, dict):
            return cls(**value)

        return super().coerce(key, value)

    @classmethod
    def to_sa_type(cls):
        return cls.as_mutable(JsonPydanticField(cls))


class Nested(MutableSABaseModel):
    a: str
    b: str | None = None


NestedSAType = Nested.to_sa_type()


class DBModel(SQLModel, table=True):

    id: str = Field(primary_key=True)
    nested: Nested = Field(sa_column=Column(NestedSAType))

[TechLipefi]

Hey there,
@tiangolo @estebanx64 could you pls make it clear, will you include it in some point in roadmap or should we come up with some workaround as @alexdashly proposed?
We also adopted SQAlchemy Mutable type, and it is working good (a bit mess with typing but it's fine)

[dadodimauro]

I had a similar problem with a list of a nested pydantic model:

from sqlmodel import JSON, Column, Field, SQLModel, create_engine

class MyNestedModel(SQLModel):
    a: str
    b: str | None

class MyModel(SQLModel):
    c: list[MyNestedModel] | None = Field(
        default=None, sa_column=Column(JSON)
    )

i solved passing to the create_engine() function the following serializer:

def serialize_pydantic_model(model: BaseModel | list[BaseModel] | None) -> str | None:
    if isinstance(model, BaseModel):
        return model.model_dump_json()
    if isinstance(model, list):
        return json.dumps([m.model_dump_json() for m in model])
    return model

I know is not an elegant solution but was enough to make it work.

[dataengineeringatfunderzgroup]

I had a similar problem with a list of a nested pydantic model:

from sqlmodel import JSON, Column, Field, SQLModel, create_engine

class MyNestedModel(SQLModel):
    a: str
    b: str | None

class MyModel(SQLModel):
    c: list[MyNestedModel] | None = Field(
        default=None, sa_column=Column(JSON)
    )

i solved passing to the create_engine() function the following serializer:

def serialize_pydantic_model(model: BaseModel | list[BaseModel] | None) -> str | None:
    if isinstance(model, BaseModel):
        return model.model_dump_json()
    if isinstance(model, list):
        return json.dumps([m.model_dump_json() for m in model])
    return model

I know is not an elegant solution but was enough to make it work.

Do you have the complete code @dadodimauro ? I'm trying to use it.

[dataengineeringatfunderzgroup]

I HAVE SOLVED!

I have created a List() field using jsonable_encoder

here is my snipped code.

SOLUTION

from fastapi.encoders import jsonable_encoder
from sqlmodel import Field, Session, SQLModel, create_engine, select, JSON, Column

class SKU(SQLModel):
    sku_item_number: str
    quantity: int
    sku_price: Decimal

class SalesBase(SQLModel):
    business_id: str = Field(index=True, description='Unique identifier for the business')
    date: datetime = Field(description='Date of the sales transaction | Format: YYYY-MM-DD')
    sale_amount: Decimal = Field(description='Total amount of sales in a given transaction')
    sale_count: int = Field(description='The number of sale transactions')
    reversal_amount: Optional[Decimal] = Field(description='Total amount of reversals (refunds) within the transaction')
    reversal_count: Optional[int] = Field(description='The number of reversals within the transaction')
    currency: str = Field(description='The currency of the transaction (e.g., USD, EUR)', default='USD')
    skus: list[SKU] | None = Field(
        default=None,
        sa_column=Column(JSON)
    )

    # Needed for Column(JSON)
    class Config:
        arbitrary_types_allowed = True

class Tb_Sales(SalesBase, table=True):
    id: uuid.UUID = Field(default_factory=uuid.uuid4, primary_key=True)


@router.post("/add")
async def add(item: SalesBase):
    tb_sales = Tb_Sales()

    with Session(engine) as session:

        # tb_sales
        tb_sales.business_id = item.business_id
        tb_sales.date = item.date
        tb_sales.sale_amount = item.sale_amount
        tb_sales.sale_count = item.sale_count
        tb_sales.reversal_amount = item.reversal_amount
        tb_sales.reversal_count = item.reversal_count
        tb_sales.currency = str(item.currency).upper()
        tb_sales.skus = jsonable_encoder(item.skus)
        session.add(tb_sales)

        # commit
        session.commit()
        session.refresh(tb_sales)

    out = {
        "message": "item created!",
        "id": tb_sales.id,
        "details": tb_sales
    }

    return out

Output data from data saved on Postgres

[
  {
    "sale_count": 10,
    "business_id": "999",
    "reversal_count": 10,
    "skus": null,
    "reversal_amount": "10",
    "date": "2024-10-31T01:42:58.469000",
    "sale_amount": "10",
    "currency": "USD",
    "id": "f10a3faf-ce80-46ea-a07c-ef7787288ad9"
  },
  {
    "sale_count": 10,
    "business_id": "999",
    "reversal_count": 10,
    "skus": [
      {
        "sku_item_number": "111",
        "quantity": 10,
        "sku_price": "10"
      }
    ],
    "reversal_amount": "10",
    "date": "2024-10-31T01:42:58.469000",
    "sale_amount": "10",
    "currency": "USD",
    "id": "2b4a04fd-8703-4e1b-a6cf-1c6b2a91d3cc"
  },
  {
    "sale_count": 20,
    "business_id": "string",
    "reversal_count": 20,
    "skus": [
      {
        "sku_item_number": "999",
        "quantity": 20,
        "sku_price": "20"
      }
    ],
    "reversal_amount": "20",
    "date": "2024-10-31T02:04:09.032000",
    "sale_amount": "20",
    "currency": "USD",
    "id": "0b76d570-83aa-48e2-8267-d39549da5fc5"
  },
  {
    "sale_count": 0,
    "business_id": "string",
    "reversal_count": 0,
    "skus": [
      {
        "sku_item_number": "string",
        "quantity": 0,
        "sku_price": "0"
      }
    ],
    "reversal_amount": "0",
    "date": "2024-10-31T02:06:38.215000",
    "sale_amount": "0",
    "currency": "USD",
    "id": "f9ae39a1-3bed-4af8-b1ed-8501d41ffa32"
  },
  {
    "sale_count": 0,
    "business_id": "string",
    "reversal_count": 0,
    "skus": [
      {
        "sku_item_number": "1",
        "quantity": 10,
        "sku_price": "10"
      },
      {
        "sku_item_number": "2",
        "quantity": 20,
        "sku_price": "20"
      },
      {
        "sku_item_number": "3",
        "quantity": 30,
        "sku_price": "30"
      }
    ],
    "reversal_amount": "0",
    "date": "2024-10-31T02:06:38.215000",
    "sale_amount": "0",
    "currency": "USD",
    "id": "f1c59936-0a8a-4134-a642-4901e75d563c"
  }
]

[gray-adeyi]

Hi! I experienced a similar issue, so I had to use @alexdashly 's solution. The first issue i experienced with the solution was that the resulting data in the database was stored as a json string even when the model that was dumped was an array which limited me from some types of queries such as fetching from the database a bunch of items based on the value in a json field, so, i had to modify it to

class JSONBPydanticField(types.TypeDecorator):
    """This is a custom SQLAlchemy field that allows easy serialization between database JSONB types and Pydantic models"""

    impl = JSONB

    def __init__(
        self,
        pydantic_model_class: type["MutableSABaseModel"],
        many: bool = False,
        *args,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        self.pydantic_model_class = pydantic_model_class
        self.many = many

    def load_dialect_impl(self, dialect):
        return dialect.type_descriptor(JSONB())

    def process_bind_param(self, value: _BaseModel | list[_BaseModel], dialect):
        """Convert python native type to JSON string before storing in the database"""
        return jsonable_encoder(value) if value else None

    def process_result_value(self, value, dialect):
        """Convert JSON string back to Python object after retrieving from the database"""
        if self.many:
            return (
                [self.pydantic_model_class.model_validate(v) for v in value]
                if value
                else None
            )
        return (
            self.pydantic_model_class.model_validate(value)
            if value is not None
            else None
        )

class MutableSAList(list, Mutable):
    """This is a hack that is intended to allow SQLAlchemy detect changes in JSON field that is a list in native python
    Allows SQLAlchmey Session to track mutable behavior"""

    @override
    def append(self, __object):
        self.changed()
        super().append(__object)

    @override
    def remove(self, __value):
        self.changed()
        super().remove(__value)

    @override
    def pop(self, __index=-1):
        self.changed()
        super().pop(__index)

    @override
    def reverse(self):
        self.changed()
        super().reverse()

    @override
    def __setattr__(self, name: str, value: Any) -> None:
        self.changed()
        super().__setattr__(name, value)

    @override
    def __setitem__(self, key, value):
        self.changed()
        super().__setitem__(key, value)

    @override
    def __delitem__(self, key):
        self.changed()
        super().__delitem__(key)

    def __iadd__(self, other):
        self.changed()
        super().__iadd__(other)


class MutableSABaseModel(_BaseModel, Mutable):
    """This is a hack that is intended to allow SQLAlchemy detect changes in JSON field that is a pydantic model"""

    def __setattr__(self, name: str, value: Any) -> None:
        """Allows SQLAlchmey Session to track mutable behavior"""
        self.changed()
        return super().__setattr__(name, value)

    @classmethod
    def coerce(cls, key: str, value: Any) -> Self | None:
        """Convert JSON to pydantic model object allowing for mutable behavior"""
        if isinstance(value, cls) or value is None:
            return value

        if isinstance(value, str):
            return cls.model_validate_json(value)

        if isinstance(value, dict):
            return cls.model_validate(value)

        if isinstance(value, list):
            return MutableSAList([cls.model_validate(v) for v in value])

        return super().coerce(key, value)

    @classmethod
    def to_sa_type(cls, many=False):
        return cls.as_mutable(JSONBPydanticField(pydantic_model_class=cls, many=many))


class BaseDBModel(SQLModel):
    id: UUID = Field(default_factory=uuid4, primary_key=True)
    created_at: AwareDatetime = Field(
        default_factory=aware_datetime_now, sa_type=TIMESTAMP(timezone=True)
    )
    last_updated_at: AwareDatetime | None = Field(sa_type=TIMESTAMP(timezone=True))

    objects: ClassVar[BaseModelManager] = BaseModelManager()

class OrganizationMemberPermission(str, Enum):
    MANAGE_EVENTS = "EVENT:WRITE"
    INVITE_MEMBERS = "MEMBERS:INVITE"
    APPROVE_REQUESTS = "MEMBERS:APPROVE_REQUEST"


class OrganizationMember(MutableSABaseModel):
    id: UUID
    role: str
    permissions: list[OrganizationMemberPermission] = Field(
        description="A list of administrative features an organization member can perform in the organization"
    )


OrganizationMembersSAType = OrganizationMember.to_sa_type(many=True)


class Organization(BaseDBModel, table=True):
    __tablename__ = "organizations"

    name: str = Field(max_length=128, unique=True)
    is_verified: bool = Field(
        False,
        description="used to flag organizations that has been verified by eventtrakka",
    )
    logo_url: str | None = Field(None)
    about: str | None
    owner_id: UUID = Field(foreign_key="users.id")
    owner: "User" = Relationship()
    members: list[OrganizationMember] = Field(
        default_factory=list,
        sa_type=OrganizationMembersSAType,
    )

    objects: ClassVar[OrganizationModelManager["Organization"]] = (
        OrganizationModelManager()
    )

The MutableSAList doesn't look great but that was what i had to fall back to after too many back and forth with chat gpt. so all json fields pydantic types had to inherit from MutableSABaseModel and I was able to perform this query. where I'm filtering organizations based on if a user is a member.

async def get_organizations_as_member(
        self,
        member: "User",
        session: AsyncSession | None = None,
    ) -> list[T]:
        async for s in get_db_session():
            session = s or session
            query = (
                select(self.model_class)
                .select_from(self.model_class)
                .join(
                    func.jsonb_array_elements(self.model_class.members).alias(
                        "members_jsonb"
                    ),
                    text("true"),  # LATERAL join
                )
                .where(
                    func.jsonb_extract_path_text(column("members_jsonb"), "id")
                    == str(member.id)
                )
            )
            return await paginate(session, query)

My current limitation is the way alembic auto generates the migrations, so i have to modify the migrations files.

# from this
sa.Column(
            "members",
            JSONBPydanticField(
                astext_type=Text(),
            ),
            nullable=False,
        ),

# to this
sa.Column(
            "members",
            JSONBPydanticField(
                pydantic_model_class=OrganizationMember,
                many=True,
                astext_type=sa.Text(),
            ),
            nullable=False,
        ),

The entire code is at https://github.com/OSCA-Ado-Ekiti/EventTrakka-Backend .

[tcztzy]

Hi! I experienced a similar issue, so I had to use @alexdashly 's solution. The first issue i experienced with the solution was that the resulting data in the database was stored as a json string even when the model that was dumped was an array which limited me from some types of queries such as fetching from the database a bunch of items based on the value in a json field, so, i had to modify it to

class JSONBPydanticField(types.TypeDecorator):
    """This is a custom SQLAlchemy field that allows easy serialization between database JSONB types and Pydantic models"""

    impl = JSONB

    def __init__(
        self,
        pydantic_model_class: type["MutableSABaseModel"],
        many: bool = False,
        *args,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        self.pydantic_model_class = pydantic_model_class
        self.many = many

    def load_dialect_impl(self, dialect):
        return dialect.type_descriptor(JSONB())

    def process_bind_param(self, value: _BaseModel | list[_BaseModel], dialect):
        """Convert python native type to JSON string before storing in the database"""
        return jsonable_encoder(value) if value else None

    def process_result_value(self, value, dialect):
        """Convert JSON string back to Python object after retrieving from the database"""
        if self.many:
            return (
                [self.pydantic_model_class.model_validate(v) for v in value]
                if value
                else None
            )
        return (
            self.pydantic_model_class.model_validate(value)
            if value is not None
            else None
        )

class MutableSAList(list, Mutable):
    """This is a hack that is intended to allow SQLAlchemy detect changes in JSON field that is a list in native python
    Allows SQLAlchmey Session to track mutable behavior"""

    @override
    def append(self, __object):
        self.changed()
        super().append(__object)

    @override
    def remove(self, __value):
        self.changed()
        super().remove(__value)

    @override
    def pop(self, __index=-1):
        self.changed()
        super().pop(__index)

    @override
    def reverse(self):
        self.changed()
        super().reverse()

    @override
    def __setattr__(self, name: str, value: Any) -> None:
        self.changed()
        super().__setattr__(name, value)

    @override
    def __setitem__(self, key, value):
        self.changed()
        super().__setitem__(key, value)

    @override
    def __delitem__(self, key):
        self.changed()
        super().__delitem__(key)

    def __iadd__(self, other):
        self.changed()
        super().__iadd__(other)


class MutableSABaseModel(_BaseModel, Mutable):
    """This is a hack that is intended to allow SQLAlchemy detect changes in JSON field that is a pydantic model"""

    def __setattr__(self, name: str, value: Any) -> None:
        """Allows SQLAlchmey Session to track mutable behavior"""
        self.changed()
        return super().__setattr__(name, value)

    @classmethod
    def coerce(cls, key: str, value: Any) -> Self | None:
        """Convert JSON to pydantic model object allowing for mutable behavior"""
        if isinstance(value, cls) or value is None:
            return value

        if isinstance(value, str):
            return cls.model_validate_json(value)

        if isinstance(value, dict):
            return cls.model_validate(value)

        if isinstance(value, list):
            return MutableSAList([cls.model_validate(v) for v in value])

        return super().coerce(key, value)

    @classmethod
    def to_sa_type(cls, many=False):
        return cls.as_mutable(JSONBPydanticField(pydantic_model_class=cls, many=many))


class BaseDBModel(SQLModel):
    id: UUID = Field(default_factory=uuid4, primary_key=True)
    created_at: AwareDatetime = Field(
        default_factory=aware_datetime_now, sa_type=TIMESTAMP(timezone=True)
    )
    last_updated_at: AwareDatetime | None = Field(sa_type=TIMESTAMP(timezone=True))

    objects: ClassVar[BaseModelManager] = BaseModelManager()

class OrganizationMemberPermission(str, Enum):
    MANAGE_EVENTS = "EVENT:WRITE"
    INVITE_MEMBERS = "MEMBERS:INVITE"
    APPROVE_REQUESTS = "MEMBERS:APPROVE_REQUEST"


class OrganizationMember(MutableSABaseModel):
    id: UUID
    role: str
    permissions: list[OrganizationMemberPermission] = Field(
        description="A list of administrative features an organization member can perform in the organization"
    )


OrganizationMembersSAType = OrganizationMember.to_sa_type(many=True)


class Organization(BaseDBModel, table=True):
    __tablename__ = "organizations"

    name: str = Field(max_length=128, unique=True)
    is_verified: bool = Field(
        False,
        description="used to flag organizations that has been verified by eventtrakka",
    )
    logo_url: str | None = Field(None)
    about: str | None
    owner_id: UUID = Field(foreign_key="users.id")
    owner: "User" = Relationship()
    members: list[OrganizationMember] = Field(
        default_factory=list,
        sa_type=OrganizationMembersSAType,
    )

    objects: ClassVar[OrganizationModelManager["Organization"]] = (
        OrganizationModelManager()
    )

The MutableSAList doesn't look great but that was what i had to fall back to after too many back and forth with chat gpt. so all json fields pydantic types had to inherit from MutableSABaseModel and I was able to perform this query. where I'm filtering organizations based on if a user is a member.

async def get_organizations_as_member(
        self,
        member: "User",
        session: AsyncSession | None = None,
    ) -> list[T]:
        async for s in get_db_session():
            session = s or session
            query = (
                select(self.model_class)
                .select_from(self.model_class)
                .join(
                    func.jsonb_array_elements(self.model_class.members).alias(
                        "members_jsonb"
                    ),
                    text("true"),  # LATERAL join
                )
                .where(
                    func.jsonb_extract_path_text(column("members_jsonb"), "id")
                    == str(member.id)
                )
            )
            return await paginate(session, query)

My current limitation is the way alembic auto generates the migrations, so i have to modify the migrations files.

# from this
sa.Column(
            "members",
            JSONBPydanticField(
                astext_type=Text(),
            ),
            nullable=False,
        ),

# to this
sa.Column(
            "members",
            JSONBPydanticField(
                pydantic_model_class=OrganizationMember,
                many=True,
                astext_type=sa.Text(),
            ),
            nullable=False,
        ),

The entire code is at https://github.com/OSCA-Ado-Ekiti/EventTrakka-Backend .

You can omit re-define the MutableList by using sqlalchemy.ext.mutable.MutableList