Pydantic json to model converter online. python cli converter json model python-library python3 cli-app pydantic Resources. if you have a strict model with a datetime field, the input must be a datetime object, but clearly that makes no sense when parsing JSON which has no datatime type. With a pydantic model with JSON compatible types, I can just do: base_model = BaseModelClass. py test script from pydantic import BaseModel, Field # Some hypothetical Pydantics types. It allows you to create data classes where you can define how data should be validated, transformed, and serialized/deserialized. - SQLAlchemy engine JSON-encodes the dict to Jun 21, 2022 · Python dataclasses are fantastic. Decimal; Validation of numeric types¶ int Pydantic uses int(v) to coerce types to an int; see Data conversion for details on loss of information during data conversion. I read all on stackoverflow with 'pydantic' w keywords, i tried examples from pydantic docs, as a last resort i generated response_model receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e. To a JSON string. last_name: str = None. class UserID(BaseModel): id: str class UserInfo(UserBase, UserID): # `UserID` should be second group: Optional[GroupInfo] = None Jul 10, 2022 · Built in JSON support . Type conversion¶ During validation, Pydantic can coerce data into expected types. SystemMessage'> at the position 1. value: int. import pandas as pd. Failed to convert text into a pydantic model due to the following error: Unexpected message with type <class 'langchain_core. those name are not allowed in python, so i want to change them to 'system_ip', 'domain_id' etc. pydantic_encoder (items) TypeError: Object of type 'list' is not JSON serializable That's strange, the list should be JSON serializable, but I'm inclined to think that it is due to pydantic_encoder misuse - it should be used only with json. Jan 2, 2020 · from typing import Optional, Annotated from pydantic import BaseModel, Field, BeforeValidator PyObjectId = Annotated[str, BeforeValidator(str)] class User_1(BaseModel): id: Optional[PyObjectId] = Field(alias="_id", default=None) All the validation and model conversions work just fine, without any class Config, or other workarounds. model_dump_json returns a JSON string representation of the dict of the schema. This may be useful if you want to serialise model. This is especially useful when you want to parse results into a type that is not a direct subclass of BaseModel. pydantic-core can parse JSON directly into a model or output type, this both improves performance and avoids issue with strictness - e. So, let's jump right in: from pydantic import BaseModel. Turns Feb 3, 2021 · Pydantic prevent conversion of incorrect type. age: int. functional_validators pydantic. class PyDanticTypeA(BaseModel): attribute_a: str attribute_b: str class PyDanticTypeB(PyDanticTypeA): attribute_c: str class PyDanticTypeC(PyDanticTypeA): attribute_d: str = Field("d") # Converting (parsing) one Feb 21, 2024 · Method 4: Using Pydantic’s from_orm. It’s worth noting that pydantic is already quite fast, though. For the deserialization process, I would use the pl. This works most of the time, but it fails in one particular case where it ignores a mandatory Field of a subclass. py as it's not just related to JSON encoding. 在FastAPI中定义Pydantic模型. in here ModelOne has three attributes one of them is a nested Pydantic Model with two attributes. json_schema pydantic. The first model should capture the "raw" data more or less in the schema you expect from the API. model_json_schema returns a dict of the schema. Pydantic’s arena is data parsing and sanitization, while Jul 7, 2021 · 1 Answer. You specify the document as a dictionary and check for validation exceptions. My question here, is there a way or a workaround to do it dynamically in runtime without using a code generator. It is included in this if TYPE_CHECKING: block since no override is actually necessary. This generates an intermediate text representation of all pydantic models which you can then dynamically import. json --input-file-type jsonschema --output cat. Mar 16, 2022 · Let’s now convert the schema into Pydantic classes by using the datamodel-codegen CLI: $ pip install datamodel-code-generator $ datamodel-codegen --input cat. You can think of models as similar to types in strictly typed languages, or as the requirements of a single endpoint in an API. 0, 1. from pydantic import BaseModel, parse_obj_as. Otherwise pydantic will try to validate the string as a Float first before passing it to the custom validator which converts empty string to None. One solution is to hack the utils out of datamodel-code-generator, specifically their JsonSchemaParser. TypeAdapter can be used to apply the parsing logic to populate Pydantic models in a more ad-hoc way. from dataclasses import dataclass. return v. I believe this is why (conveniently) it is possible to assign the value of an int enum property of a class via its raw int value. Hi I am trying to create a list of BaseModel objects and then convert that list to a json string. However when I use json. class S(str, Enum): Jan 25, 2021 · from typing import Type from pydantic import BaseModel from pydantic. I would say that comparing these two great modules is like comparing pears with apples, albeit similar in some regards, different overall. Jul 10, 2022 · In a FastAPI operation you can use a Pydantic model directly as a parameter. Sep 27, 2022 · Stack Overflow Public questions & answers; Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Talent Build your employer brand Sequence vs list or tuple - Mapping vs dict. from_json_schema classmethod that could dynamically create a model like so Oct 30, 2023 · Construct Pydantic models from json at Runtime. The specific configuration frozen (in beta) has a special meaning. __pydantic Dec 14, 2023 · My thinking has been that I would take the json output from that method and read it back in via the python json library, so that it becomes a json-serializeable dict. Pydantic model and dataclasses. We'll also define our own class attribute __csv_separator__ to hold the string that we will split records with. from pydantic import BaseModel. May 4, 2021 · 3. Being able to serialize a list of models would still be needed either way for fields of a type like that, e. To generate a Pydantic model from a JSON object, enter it into the JSON editor and watch a Pydantic model automagically appear in Mar 1, 2023 · I'm attempting to deserialize a Pydantic model instance using the schema stored in model. from pydantic import BaseModel class Post_Response(BaseModel): name: str class Post_Mother(BaseModel): status: int users: List[Post_Response] = [] let import it into our app. Pydantic's BaseModel 's dict method has exclude_defaults and exclude_none options for: exclude_defaults: whether fields which are equal to their default values (whether set or otherwise) should be excluded from the returned dictionary; default False. BaseModel; pydantic_v2. Sorted by: 1. Sep 24, 2022 · It's easy to write code to parse JSON into create_model arguments, and it would make sense to use the output of BaseModel. Note: this means that attributes on the model with defaults of this type, not annotations of this type, will be left alone. IMPORTANT you are assigning your dictionary to the Python dict type! Use a different variable name other than 'dict', like below I made it 'data_dict'. messages. Pydantic is fantastic. I would like to flatten and remap the ORM model to eliminate an unnecessary level in the JSON. Which stores the models’ definitions in cat. 下面是一个示例模型的定义：. parse_obj()` function. This means, whenever you are dealing with the student model id, in the database this will be stored as _id field name instead. This is useful especially when we have complex nested data. Body of the response object is accessible via response. type_adapter pydantic. [] With just that Python type declaration, FastAPI will: Read the body of the request as JSON. Jan 30, 2020 · pip install fastapi_camelcase. Jan 13, 2022 · The default API response is simple json like object from response_model. We will print the resulting dictionary. Field Type Conversions — strict and lax conversion between different field types. name: Optional[StrictStr] = None. Nov 3, 2021 · import json to pydantic model, change fiield name. aliases. dict () JSON-like serialization on . Using Pydantic, there are several ways to generate JSON schemas or JSON representations from fields or models: BaseModel. in the Jun 21, 2022 · While in Pydantic, the underscore prefix of a field name would be treated as a private attribute. dump). model_dump() for res in data]) The main point is iterate the list and transform it to a dictionary with pydantinc built-in function model_dump. You can force them to run with Field (validate_default=True). Moreover, the attribute must actually be named key and use an alias (with Field ( alias="_key" ), as pydantic treats underscore-prefixed fields as internal and does not expose them. fields pydantic. dict () later (default: False) from enum import Enum. However, when I run the script, instead of printing the data as expected, the script prints the schema from model. pd. I would probably go with a two-stage parsing setup. target. setOptions({ options, snakeCased: e. class Dummy(BaseModel): id: Optional[StrictInt] = None. core_schema Pydantic Settings Pydantic Settings Pydantic supports the following numeric types from the Python standard library: int; float; enum. user. For example, the dictionary might look like this: { "hello": MyPydanticModel(name="hello"), "there": MyPydanticModel(name="there") } Jan 13, 2022 · When I want to reload the data back into python, I need to decode the JSON (or BSON) string into a pydantic basemodel. When using the second version to declare frozen=True (with keyword arguments in the class definition), Pylance can use it to help you check in your code and detect errors when something is trying to set values in a model that is Oct 12, 2021 · Look for Pydantic's parameter "use_enum_values" in Pydantic Model Config. If you don't need data validation that pydantic offers, you can use data classes along with the dataclass-wizard for this same task. There are two modes of coercion: strict and lax. – Mar 24, 2021 · It guarantees the types and constraints of the model have been applied and that the data is valid. IntEnum; decimal. JSON is the de-facto data interchange format of the internet, and Pydantic is a library that makes parsing JSON in Python a breeze. model_dump (by_alias = True)) #> {'metadata_': {'key': 'val'}} Pydantic provides builtin JSON parsing, which helps achieve: Significant performance improvements without the cost of using a 3rd party library. data = [SomeModel(**{'col1': 'foo', 'col2': 'bar'})] * 10. You might reasonably balk at this, but it does allow for self-referencing and multi-model setups at least: Utility for converting json files to Pydantic models - temkuz/json_pydantic. Using model_dump with mode='json', x is serialized as a string, and y is serialized as a float because of the custom serializer applied. Sep 19, 2021 · doing only a cursory look on the web, it looks like this is a known problem that pydantic doesn't support loading nested json to a model class, yet there are plans for future support in this use case. As you can see, for the above dataset pydantic is about 2x slower in both the deserialization and serialization process. 0. I am trying to serialize a pydantic model to JSON with the model_dump_json () function. Simple example below: from __future__ import annotations. json. The reason is to allow users to recreate the original model from the schema without having the original files. Raises: ValueError: When the Schema Definition is not a Tuple/Dictionary. ensure_tzinfo @classmethod def ensure_tzinfo(cls, v): # if TZ isn't provided, we assume UTC, but you can do w/e you need if it's as simple as getting dict and unpacking that dict to another model. py to pydantic/serialisation. Having a model as entry let you work with the object and not the parameters of a ditc/json. parse_obj()` function can be used to convert a JSON string to a pydantic model. Here's an example of Pydantic's builtin JSON parsing via the model_validate_json method, showcasing the support for strict specifications while parsing JSON data that doesn't match the model's type Mar 12, 2021 · I have a data structure which consists of a dictionary with string keys, and the value for each key is a Pydantic model. Feb 12, 2021 · I am trying to create a dynamic model using Python's pydantic library. py is: { "foo": "bar" } But the actual output is: Aug 19, 2022 · 2. class SomeModel(BaseModel): col1: str. However I have a scenario where I want to avoid this behaviour Mar 9, 2021 · from datetime import datetime, timezone from pydantic. According to the FastAPI tutorial: To declare a request body, you use Pydantic models with all their power and benefits. types. For example: Oct 30, 2021 · Args: name (str): The Model Name that you wish to give to the Pydantic Model. dict () it unpacks the key, value pairs of that dictionary Apr 19, 2023 · Another alternative is to pass the multiplier as a private model attribute to the children, then the children can use the pydantic validation function, but you'll still need to assign dynamically to the children: multiplier: int # exclude from parent serialization, workaround for validation. samuelcolvin changed the title Datetime serialization on . ModelTwo has two attributes one of which is another Pydantic Model with one attribute. That makes me think that there should already be some sort of BaseModel. Dec 9, 2020 · 156. Python. Dec 20, 2021 · from pydantic import BaseModel, EmailStr from uuid import UUID, uuid4 class User(BaseModel): name: str last_name: str email: EmailStr id: UUID = uuid4() However, all the objects created using this model have the same uuid, so my question is, how to gen an unique value (in this case with the id field) when an object is created using pydantic. class MyModel(BaseModel): my_enum_field: MyEnum. schema_extra a dict used to extend/update the generated JSON Schema, or a callable to post-process it; see schema customization json_loads a custom function for decoding JSON; see custom JSON (de)serialisation json_dumps Apr 30, 2022 · I would like to validate a pydantic field based on that enum. functional_serializers pydantic. However, you are generally better off using a @model_validator (mode='before') where the function is Pydantic Dataclasses TypeAdapter validate_call Fields Config json_schema Errors Functional Validators Functional Serializers Pydantic Types Network Types Version Information Pydantic Core Pydantic Core pydantic_core pydantic_core. In addition to that value, I want the model to output all possible values from that enum (those enums are range-like, e. checked })"," }"," />"," Alias camelCase fields as snake_case"," Jul 6, 2021 · I have a model ModelWithEnum that holds an enum value. json import pydantic_encoder # -----# Define pydantic-alchemy specific types (once per application) # -----class PydanticType (sa. We can use Pydantic to get better typed code and also add validators ensuring lesser errors. types pydantic. model_validate , but works with arbitrary Pydantic-compatible types. Untrusted data can be passed to a model, and after parsing and validation pydantic guarantees Jan 8, 2021 · Pydantic 1. Metadata for generic models; contains data used for a similar purpose to args, origin, parameters in typing-module generics. See the documentation of BaseModel. pydantic. Dec 10, 2021 · 3. loads decoder doesn't know how to deal with a May 20, 2021 · I think I arrive a little bit late to the party, but I think this answer may come handy for future users having the same question. i keep getting this for everything, just to give some context it doesnt work with gemini at all and it used to work, alot of gemini projects of many diffrnet people that Sep 6, 2023 · Python pydantic V2 model_dump_json for subclass leaves out Field. class Model(BaseModel): the_id: UUID = Field(default_factory=uuid4) Sep 24, 2019 · I have a working model to receive a json data set using pydantic. Using model_dump_json, x is serialized as a string, and y is serialized as a float because of the custom serializer applied. You need to use use_enum_values option of model config: use_enum_values. root_model pydantic. Here's a simplified example to illustra Pydantic provides functionality to serialize model in three ways: To a Python dict made up of the associated Python objects. This function behaves similarly to BaseModel. model_dump for more details about the arguments. errors pydantic. AliasGenerator. class ORMSimulatedUser: def __init__(self, name, age): Using an AliasGenerator¶ API Documentation. read_json() method to produce a dataframe. Enum¶ Pydantic uses Python's standard enum classes to define choices. 可以使用Pydantic的 BaseModel 类来定义模型。. parse_raw(string) But the default json. Note that the by_alias keyword argument defaults to False , and must be specified explicitly to dump models using the field (serialization) aliases. Aug 19, 2021 · I am using Pydantic with FastApi to output ORM data into JSON. my-test. version Pydantic Core Pydantic Core pydantic_core pydantic_core. datetime_parse import parse_datetime class utc_datetime(datetime): @classmethod def __get_validators__(cls): yield parse_datetime # default pydantic behavior yield cls. For such a simple thing as excluding None -valued fields in the JSON representation, you can simply use the built-in exclude_none parameter: from typing import Optional. AliasGenerator is a class that allows you to specify multiple alias generators for a model. json and data stored in data. model_validate (sql_model) print (pydantic_model. from starlette. The "Strict" column contains checkmarks for type conversions that are allowed when validating in Strict Mode. In all three modes, the output can be customized by excluding specific fields, excluding unset fields, excluding default values, and excluding None pydantic. dict () to convert the model to a Python dictionary. It prevents other code from changing a model instance once it's created, keeping it "frozen". Here is your solution: The following table provides details on how Pydantic converts data during validation in both strict and lax modes. py . Returns: pydantic. Setting validate_default to True has the closest behavior to using always=True in validator in Pydantic v1. When using Sequence, Pydantic calls isinstance (value, Sequence) to check if the value is a sequence. dataclass generator for easy conversion of JSON, OpenAPI, JSON Schema, and YAML data sources. Model: A Pydantic Model. You need to use the Pydantic method . Allowed values: 0. Sub model has to inherit from pydantic. The `pydantic. whether to populate models with the value property of enums, rather than the raw enum. TypeDecorator): """Pydantic type. version Question. JSON, or JavaScript Object Notation, is a lightweight data-interchange format that is easy for humans to read and write. dict () later (default: False) It looks like setting this value to True will do the same as the below solution. json. Data validation using Python type hints. when you just do **instance1. Dec 14, 2023 · Pydantic is a data validation and settings management library using Python type annotations. You need to decouple the id field from UserInfo model as. My input data is a regular dict. I am wondering how to dynamically create a pydantic model which is dependent on the dict's content? I created a toy example with two different dicts (inputs1 and inputs2). col2: str. parse_obj_as requires dictionary input. But in this case, I am not sure this is a good idea, to do it all in one giant validation function. responses import JSONResponse. Although this method can receive some optional inputs to customize the conversion of the model to a dictionary, for this test we will pass no arguments, so we get the default behavior. and also to convert and filter the output data to its type declaration. I cannot make this field as a string field Mar 22, 2023 · CSVType = Union[CSVTypeA, CSVTypeB, CSVTypeC] Next we define the model to represent the actual CSV file as a custom root type, which will be a list of our discriminated union of record types. Convert the corresponding types (if needed You can use the Json data type to make Pydantic first load a raw JSON string before validating the loaded data into the parametrized type: ```py group='json' from typing import Any, List from pydantic import BaseModel, Json, ValidationError Dec 14, 2022 · If you want to convert a Pydantic object/type to another Pydantic object/type. SAVING: - Uses SQLAlchemy JSON type under the hood. May eventually be replaced by these. May 26, 2021 · Solution #3: Declare as All-Optional But Manually Validate for POST. a list of Pydantic models, like List[Item]. May 19, 2022 · >> > pydantic. - koxudaxi/datamodel-code-generator The primary means of defining objects in pydantic is via models (models are simply classes which inherit from BaseModel ). Mar 22, 2022 · Validation can be done by using the pydantic parse_obj method of the model. exemple of object parameters: for mon in RestaurantSchedule. Then I would somehow attach this "encoder" to the pydantic json method. Dec 15, 2022 · Pydantic provides root validators to perform validation on the entire model's data. In this case, we fetch all the documents (up to the specified limit) using a Couchbase query and test them one by one and report any errors. There are two ways to convert JSON data to a pydantic model: Using the `pydantic. mypy pydantic. To convert the dataclass to json you can use the combination that you are already using using (asdict plus json. However, the content of the dict (read: its keys) may vary. from pydantic import BaseModel, StrictInt, StrictStr. dict( exclude_unset =True) In this line of code, “p_instance” is a Pydantic model instance that represents the data, and “p_dict” is a resulting dictionary. dict_def (dict): The Schema Definition using a Dictionary. BUT I would like this validation to also accept string that are composed by the Enum members. Looking to automate your workflow? Check our API. You can see more details about model_dump in the API reference. BaseModel; dataclasses. body. BaseModel): foo: int # <-- like this ``` We also account for the case where the annotation can be an instance of `Annotated` and where one of the (not first) arguments in `Annotated` is an instance of Apr 19, 2019 · here is the pydantic model model. 1. To a Python dict made up only of "jsonable" types. The expected output from test_load. The datamodel-code-generator project is a library and command-line utility to generate pydantic models from just about any data source, including: OpenAPI 3 (YAML/JSON) JSON Schema; JSON/YAML/CSV Data (which will converted to JSON Schema) Python dictionary (which will be converted to JSON Schema) GraphQL schema Converting JSON to a pydantic model. monday: print(mon) Jun 3, 2022 · For exemplification purposes, we will now convert our model object back to a dictionary with a call to the dict method. So for example : "val1_val2_val3" or "val1_val3" are valid input. validate_call pydantic. I was actually surprised that pydantic doesn't parse a dict to a nested model - seems like a common enough use case to me. This used to work in v1 by using create_model on the schema dump of the Oct 6, 2023 · Here, we will see how to convert a Pydantic model instance to a dictionary while excluding the fields with default values. loads (). 要在FastAPI中处理JSON数组，我们首先需要定义一个Pydantic模型来表示一个JSON对象。. use_enum_values whether to populate models with the value property of enums, rather than the raw enum. Pydantic can convert ORM model instances to Pydantic models using the from_orm method. JSON) sql_model = SQLModel (metadata_ = {'key': 'val'}, id = 1) pydantic_model = MyModel. Pydantic appears to perform automatic type conversion when the type of a property is not what is expected. system. The alias is defined so that the _id field can be referenced. 56} What I would like to do is have a list of json files as the data set and be able to validate them. parse_obj()` function; Using the `pydantic. core_schema Pydantic allows automatic creation of JSON schemas from models. dumps(, default=pydantic_encoder) . dataclass; This function is used internally to create a `FieldInfo` from a bare annotation like this: ```python import pydantic class MyModel(pydantic. But you can use the following trick: Note: This is a suboptimal solution. Nested environment variables take precedence over the top-level environment variable JSON (e. float similarly, float(v) is used to coerce values to floats We are using model_dump to convert the model into a serializable format. You can use an AliasGenerator to specify different alias generators for validation and serialization. The StudentModel utilises _id field as the model id called id. See Conversion Table for more details on how Pydantic converts data in both strict and lax modes. core_schema Pydantic Settings Pydantic Settings This applies both to @field_validator validators and Annotated validators. py. Also, Pydantic will try to validate against different types of sequences, like list and tuple . This is particularly useful when working with databases and ORMs, such as SQLAlchemy. It is a tough choice if indeed we are confronted with choosing one or the other. from pydantic. The following stripped-down code fragment illustrates the problem: basename:str. Allowed values: 0, 1. x (old answer) The current version of pydantic does not support creating jsonable dict straightforwardly. BaseModel. Nov 17, 2021 · 0. And you need to transform bytes type of body to dictionary by calling json. - Acceps the pydantic model and converts it to a dict on save. from typing import List from pydantic import BaseModel class User(BaseModel): name: str. This method is included just to get a more accurate return type for type checkers. If you know the value is a list or tuple, use list or tuple instead of Sequence. types import PositiveInt. We no longer need to parse JSON’s to dictionaries. dict () on Jun 26, 2020. BaseModel. dataclasses import dataclass as pydantic_dataclass from typing import List from dataclasses import dataclass def model_from_dataclass(kls: 'StdlibDataclass') -> Type[BaseModel]: """Converts a stdlib dataclass to a pydantic BaseModel""" return pydantic_dataclass(kls). py, not only adding the configurations but with proper typing in Optionals and field Apr 19, 2021 · class Model(BaseModel): x: Union[None, float] @validator('*', pre=True) def empty_str_to_none(cls, v): if v == '': return None. It's slightly easier as you don't need to define a mapping for lisp-cased keys such as server-time. However, Pydantic does not seem to register those as model fields. schema_json() since that already defines a serialization format. : Sep 23, 2021 · In their docs, pydantic claims to be the fastest library in general, but it's rather straightforward to prove otherwise. Source code in pydantic/root_model. from_json()` method; The `pydantic. The model data set looks like this: data = {'thing_number': 123, 'thing_description': 'duck', 'thing_amount': 4. DataFrame([res. class Reservation(BaseModel): date: str = Field(description="reservation date") Oct 12, 2022 · Sorted by: 1. import json. Oct 9, 2023 · The Pydantic (JSON) Parser. g. Oct 27, 2023 · I have a fairly complex pydantic model that I want to convert the schema of to its own Pydantic BaseModel to return as a response_model in a FastAPI endpoint. from fastapi_camelcase import CamelModel class User(CamelModel): first_name: str. Since you are using fastapi and pydantic there is no need to use a model as entry of your route and convert it to dict. In order to tell LangChain that we'll need to convert the text to a Pydantic object, we'll need to define the Reservation object first. The full code for this example can be found here JSON to Pydantic is a tool that lets you convert JSON objects into Pydantic models. May 15, 2020 · I use the following way. dumps(my_list) I get TypeError: Object of type User is not JSON serializable. From Pydantic documentation, it's described how to statically create a Pydantic model from a json description using a code generator called datamodel-code-generator. env_nested_delimiter can be configured via the model_config as shown above, or via the _env_nested_delimiter keyword argument on instantiation. Here’s an example: from pydantic import BaseModel. I have json, from external system, with fields like 'system-ip', 'domain-id'. Feb 29, 2024 · from pydantic. FastAPI will use this response_model to do all the data documentation, validation, etc. Convert your 3D models to multiple formats (OBJ, FBX, USDZ, GLB, GLTF, and more) online, free, and safe. Aug 5, 2020 · My thought was then to define the _key field as a @property -decorated function in the class. . networks pydantic. Readme As I mentioned in my comment, it doesn't really matter the order of the JSON, but when it comes to schema generation, it can be helpful. OpenAPI 3 (YAML/JSON, OpenAPI Data Type); JSON Schema (JSON Schema Core/JSON Schema Validation); JSON/YAML/CSV Data (it will be converted to JSON Schema); Python dictionary (it will be converted to JSON Schema); GraphQL schema (GraphQL Schemas and Types); Supported output types. model_dump ()) #> {'metadata': {'key': 'val'}} print (pydantic_model. from fastapi import FastAPI, Response, status, HTTPException, Depends from in v2, rename pydantic/json. p_dict = p_instance. jqkuvjoeoxyqcftojfid