@ -153,48 +153,16 @@ And you could do this even if the data type in the request is not JSON.
For example, in this application we don't use FastAPI's integrated functionality to extract the JSON Schema from Pydantic models nor the automatic validation for JSON. In fact, we are declaring the request content type as YAML, not JSON:
For example, in this application we don't use FastAPI's integrated functionality to extract the JSON Schema from Pydantic models nor the automatic validation for JSON. In fact, we are declaring the request content type as YAML, not JSON:
In Pydantic version 1 the method to get the JSON Schema for a model was called `Item.schema()`, in Pydantic version 2, the method is called `Item.model_json_schema()`.
///
Nevertheless, although we are not using the default integrated functionality, we are still using a Pydantic model to manually generate the JSON Schema for the data that we want to receive in YAML.
Nevertheless, although we are not using the default integrated functionality, we are still using a Pydantic model to manually generate the JSON Schema for the data that we want to receive in YAML.
Then we use the request directly, and extract the body as `bytes`. This means that FastAPI won't even try to parse the request payload as JSON.
Then we use the request directly, and extract the body as `bytes`. This means that FastAPI won't even try to parse the request payload as JSON.
And then in our code, we parse that YAML content directly, and then we are again using the same Pydantic model to validate the YAML content:
And then in our code, we parse that YAML content directly, and then we are again using the same Pydantic model to validate the YAML content:
In Pydantic version 1 the method to parse and validate an object was `Item.parse_obj()`, in Pydantic version 2, the method is called `Item.model_validate()`.
In Pydantic v1 it came included with the main package. Now it is distributed as this independent package so that you can choose to install it or not if you don't need that functionality.
///
### Create the `Settings` object { #create-the-settings-object }
### Create the `Settings` object { #create-the-settings-object }
Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.
Import `BaseSettings` from Pydantic and create a sub-class, very much like with a Pydantic model.
@ -60,24 +54,8 @@ The same way as with Pydantic models, you declare class attributes with type ann
You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.
You can use all the same validation features and tools you use for Pydantic models, like different data types and additional validations with `Field()`.
The `Config` class is used just for Pydantic configuration. You can read more at <ahref="https://docs.pydantic.dev/1.10/usage/model_config/"class="external-link"target="_blank">Pydantic Model Config</a>.
///
////
/// info
In Pydantic version 1 the configuration was done in an internal class `Config`, in Pydantic version 2 it's done in an attribute `model_config`. This attribute takes a `dict`, and to get autocompletion and inline errors you can import and use `SettingsConfigDict` to define that `dict`.
///
Here we define the config `env_file` inside of your Pydantic `Settings` class, and set the value to the filename with the dotenv file we want to use.
Here we define the config `env_file` inside of your Pydantic `Settings` class, and set the value to the filename with the dotenv file we want to use.
### Creating the `Settings` only once with `lru_cache` { #creating-the-settings-only-once-with-lru-cache }
### Creating the `Settings` only once with `lru_cache` { #creating-the-settings-only-once-with-lru-cache }
If you have an old FastAPI app, you might be using Pydantic version 1.
If you have an old FastAPI app, you might be using Pydantic version 1.
FastAPI has had support for either Pydantic v1 or v2 since version 0.100.0.
FastAPI version 0.100.0 had support for either Pydantic v1 or v2. It would use whichever you had installed.
If you had installed Pydantic v2, it would use it. If instead you had Pydantic v1, it would use that.
FastAPI version 0.119.0 introduced partial support for Pydantic v1 from inside of Pydantic v2 (as `pydantic.v1`), to facilitate the migration to v2.
Pydantic v1 is now deprecated and support for it will be removed in the next versions of FastAPI, you should **migrate to Pydantic v2**. This way you will get the latest features, improvements, and fixes.
FastAPI 0.126.0 dropped support for Pydantic v1, while still supporting `pydantic.v1` for a little while.
/// warning
/// warning
Also, the Pydantic team stopped support for Pydantic v1 for the latest versions of Python, starting with **Python 3.14**.
The Pydantic team stopped support for Pydantic v1 for the latest versions of Python, starting with **Python 3.14**.
This includes `pydantic.v1`, which is no longer supported in Python 3.14 and above.
If you want to use the latest features of Python, you will need to make sure you use Pydantic v2.
If you want to use the latest features of Python, you will need to make sure you use Pydantic v2.
///
///
If you have an old FastAPI app with Pydantic v1, here I'll show you how to migrate it to Pydantic v2, and the **new features in FastAPI 0.119.0** to help you with a gradual migration.
If you have an old FastAPI app with Pydantic v1, here I'll show you how to migrate it to Pydantic v2, and the **features in FastAPI 0.119.0** to help you with a gradual migration.
## Official Guide { #official-guide }
## Official Guide { #official-guide }
@ -44,7 +46,7 @@ After this, you can run the tests and check if everything works. If it does, you
## Pydantic v1 in v2 { #pydantic-v1-in-v2 }
## Pydantic v1 in v2 { #pydantic-v1-in-v2 }
Pydantic v2 includes everything from Pydantic v1 as a submodule `pydantic.v1`.
Pydantic v2 includes everything from Pydantic v1 as a submodule `pydantic.v1`. But this is no longer supported in versions above Python 3.13.
This means that you can install the latest version of Pydantic v2 and import and use the old Pydantic v1 components from this submodule, as if you had the old Pydantic v1 installed.
This means that you can install the latest version of Pydantic v2 and import and use the old Pydantic v1 components from this submodule, as if you had the old Pydantic v1 installed.
# Separate OpenAPI Schemas for Input and Output or Not { #separate-openapi-schemas-for-input-and-output-or-not }
# Separate OpenAPI Schemas for Input and Output or Not { #separate-openapi-schemas-for-input-and-output-or-not }
When using **Pydantic v2**, the generated OpenAPI is a bit more exact and **correct** than before. 😎
Since **Pydantic v2** was released, the generated OpenAPI is a bit more exact and **correct** than before. 😎
In fact, in some cases, it will even have **two JSON Schemas** in OpenAPI for the same Pydantic model, for input and output, depending on if they have **default values**.
In fact, in some cases, it will even have **two JSON Schemas** in OpenAPI for the same Pydantic model, for input and output, depending on if they have **default values**.
@ -100,5 +100,3 @@ And now there will be one single schema for input and output for the model, only
That extra info will be added as-is to the output **JSON Schema** for that model, and it will be used in the API docs.
That extra info will be added as-is to the output **JSON Schema** for that model, and it will be used in the API docs.
//// tab | Pydantic v2
You can use the attribute `model_config` that takes a `dict` as described in <ahref="https://docs.pydantic.dev/latest/api/config/"class="external-link"target="_blank">Pydantic's docs: Configuration</a>.
In Pydantic version 2, you would use the attribute `model_config`, that takes a `dict` as described in <ahref="https://docs.pydantic.dev/latest/api/config/"class="external-link"target="_blank">Pydantic's docs: Configuration</a>.
You can set `"json_schema_extra"` with a `dict` containing any additional data you would like to show up in the generated JSON Schema, including `examples`.
You can set `"json_schema_extra"` with a `dict` containing any additional data you would like to show up in the generated JSON Schema, including `examples`.
////
//// tab | Pydantic v1
In Pydantic version 1, you would use an internal class `Config` and `schema_extra`, as described in <ahref="https://docs.pydantic.dev/1.10/usage/schema/#schema-customization"class="external-link"target="_blank">Pydantic's docs: Schema customization</a>.
You can set `schema_extra` with a `dict` containing any additional data you would like to show up in the generated JSON Schema, including `examples`.
////
/// tip
/// tip
You could use the same technique to extend the JSON Schema and add your own custom extra info.
You could use the same technique to extend the JSON Schema and add your own custom extra info.
Sebastián Ramírez
6 months ago
GitHub