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python fastapi annotated type for data validation metadata

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fastapi is a modern web framework for building apis with python 3.7+ based on standard python type hints. one of the powerful features of fastapi is its support for data validation and serialization using pydantic models. pydantic uses python's type annotations to validate data and provide helpful error messages.
what are annotated types?
annotated types in python allow you to attach metadata to type hints. this can be useful for various purposes, such as specifying additional validation rules or constraints. fastapi leverages this feature to enhance data validation and provide better documentation.
using pydantic with fastapi
fastapi uses pydantic models to validate request and response data. you can define data models using standard python data types and add validation by using pydantic's features.
step-by-step tutorial
step 1: install fastapi and uvicorn
if you haven’t installed fastapi and uvicorn, you can do so using pip:
step 2: create a pydantic model
you can create a pydantic model to define the structure of your data. here's an example where we define a simple `user` model with some validation:
- `constr`: a string constrained by length.
- `emailstr`: a pydantic type that validates the format of an email address.
step 3: create a fastapi application
now, let's create a fastapi application that uses the `user` model to validate incoming data:
step 4: running the application
you can run the application with uvicorn:
make sure to replace `your_module_name` with the name of your python file (without the `.py` extension).
step 5: testing the api
here’s an example of a json request to create a user:
step 6: handling validation errors
if the data does not meet the validation criteria, fastapi automatically returns a 422 unprocessable entity response with details about the validation errors. for example ...
#FastAPI #Python #DataValidation
FastAPI
Python
data validation
type annotations
Pydantic
request validation
response model
data models
API validation
type hints
metadata
asynchronous programming
schema validation
OpenAPI
input validation
what are annotated types?
annotated types in python allow you to attach metadata to type hints. this can be useful for various purposes, such as specifying additional validation rules or constraints. fastapi leverages this feature to enhance data validation and provide better documentation.
using pydantic with fastapi
fastapi uses pydantic models to validate request and response data. you can define data models using standard python data types and add validation by using pydantic's features.
step-by-step tutorial
step 1: install fastapi and uvicorn
if you haven’t installed fastapi and uvicorn, you can do so using pip:
step 2: create a pydantic model
you can create a pydantic model to define the structure of your data. here's an example where we define a simple `user` model with some validation:
- `constr`: a string constrained by length.
- `emailstr`: a pydantic type that validates the format of an email address.
step 3: create a fastapi application
now, let's create a fastapi application that uses the `user` model to validate incoming data:
step 4: running the application
you can run the application with uvicorn:
make sure to replace `your_module_name` with the name of your python file (without the `.py` extension).
step 5: testing the api
here’s an example of a json request to create a user:
step 6: handling validation errors
if the data does not meet the validation criteria, fastapi automatically returns a 422 unprocessable entity response with details about the validation errors. for example ...
#FastAPI #Python #DataValidation
FastAPI
Python
data validation
type annotations
Pydantic
request validation
response model
data models
API validation
type hints
metadata
asynchronous programming
schema validation
OpenAPI
input validation